Towards Gender Biased Language Classification: A Case Study with
British English Archival Metadata Descriptions
Lucy Havens
School of Informatics / University of Edinburgh
Informatics Forum / 10 Crichton Street
Edinburgh, United Kingdom / EH8 9AB
lucy.havens@ed.ac.uk
Abstract
This thesis-in-progress summarizes the work
completed and potential directions for a Ph.D.
project researching the classification of genderbiased language. Recognizing bias as inherent in language and thus inevitable in natural
language processing systems, the project aims
to make bias transparent. An interdisciplinary
methodology is applied to define gender bias,
annotate documents according to that definition, and train classification models on the annotated dataset to identify types of gender bias.
Having created a gender biased language taxonomy and an annotated dataset, the project now
moves towards the development of documentclassification models. There are several directions the classifier development could follow.
The project would benefit from participationin the Student Research Workshop to discuss
which direction would add the most valuable
contribution to computational linguistics.

1

Introduction and BackgroundThe need to mitigate bias in data has become urgent
as evidence of harms from such data grows (Perez,
2019; Noble, 2018). Due to the complexities ofbias often overlooked in natural language processing (NLP) bias research (Devinney et al., 2022;
Stańczak and Augenstein, 2021), Blodgett et al.
(2020) and Crawford (2017) call for greater interdisciplinary collaboration and stakeholder involvement in NLP and machine learning (ML) research.
The gallery, library, archive, and museum (GLAM)
sector has made similar calls for interdisciplinary
engagement, looking to applications of data science and ML to better understand and mitigate
bias in GLAM collections (Padilla, 2017, 2019;
Geraci, 2019). Supporting the NLP and GLAM
communities’ shared aim of of mitigating the minoritization1 of certain social groups that biased
1

D’Ignazio and Klein (2020) propose the term “minoritization” to describe a group of people’s experience of oppression,in place of “minority” which defines people as oppressed.

language causes, this project aims to develop a classification model that categorizes biased language in
GLAM documentation. The project uses the term
biased language to refer to “written or spoken language that creates or reinforces inequitable power
relations among people, harming certain people
through simplified, dehumanizing, or judgmental
words or phrases that restrict their identity; andprivileging other people through words or phrases
that favor their identity” (Havens et al., 2020). The
project uses the term GLAM documentation to refer to the descriptions of cultural heritage collection items written in catalogs of galleries, libraries,
archives, and museums. Figure 3 in A.9 shows anexample of GLAM documentation online.
Studying GLAM documentation provides an opportunity to study the evolution of biased language,
because descriptions in contemporary GLAM catalogs contain both historical and contemporary language. To provide a record of the past, GLAM
continually acquire and describe heritage items,
structuring descriptions of the items according to
metadata standards (such as Research Description
and Access (RDA Steering Committee, 2022)) andsubject authorities (such as Library of Congress
Subject Headings (Library of Congress, 2021)).
The heritage items included in GLAM, along withthe language used to describe them in catalogs,
have a continual influence on society (Benjamin,
2019; Cook, 2011; Smith, 2006). The processesof selecting which items to bring into GLAM, andorganizing those items according to standards andauthorities, privilege particular perspectives (Adler,
2017; de Jong and Koevoets, 2013; Furner, 2007;
Tanselle, 2002; Olson, 2001; Bowker and Star,
1999). These processes shape society’s understanding of the present and can either reinforce or challenge existing power relationships among people(Benjamin, 2019; Noble, 2018; Yale, 2015; de Jongand Koevoets, 2013; Cook, 2011; Smith, 2006).
Through case studies of free-text descriptions inmany GLAM catalogs, variations in biased language over time and across locations could bebetter understood. Should patterns in the evolution of biased language emerge, language technology could one day be trained to identify newlyemerging types of bias that it has not yet seen. This
project takes the first step in that direction, witha case study of biased language classification for
GLAM documentation.
To create biased language classifiers, the project
defined three objectives:
O1. Define types of bias for GLAM.
O2. Measure the prevalence of biased language in
GLAM documentation.
O3. Build and evaluate classifiers to detect bias.
O1 has been achieved and O2 is in progress (§4).
As the project proceeds, several approaches are under consideration for building and evaluating classifiers (§5). Recently passing the halfway point ofa three-and-half-year Ph.D., the project would benefit from feedback at the Student Research Workshop to discuss approaches to O3.

2

Related Work

Awareness of limitations in approaches to bias mitigation in Natural Language Processing (NLP) andthe wider Machine Learning (ML) community is
growing. Publications about NLP bias research
now include not only efforts to debias datasets
and algorithms (Webster et al., 2018; Zhao et al.,2018), but also recommendations to address thecomplexity of bias that debiasing efforts often miss(Goldfarb-Tarrant et al., 2021; Blodgett et al., 2021;
Jo and Gebru, 2020; Havens et al., 2020; Gonen andGoldberg, 2019; Mitchell et al., 2019; Bender andFriedman, 2018). Recognizing the harmful impactsof deep learning models trained on datasets toolarge to be adequately interrogated (Birhane andPrabhu, 2021; Bender et al., 2021; Noble, 2018),
this project will train supervised NLP models on adataset small enough to be interrogated (399,957
words, 24,474 sentences). Moreover, collaborators
include archivists who manage the collections described in the project’s data and have expert knowledge to inform annotation and analysis processes.
Recognizing the subjective nature of certain
NLP tasks, such as detecting hate speech and bias,
Davani et al. (2022), Sang and Stanton (2022), andBasile et al. (2021) have questioned annotation
approaches that create a single gold standard or
ground truth dataset. The “perspectivist” approach

to NLP this inspired, which incorporates multiple annotators’ perspectives in published datasets(Basile, 2022), aligns with the data feminist approach that D’Ignazio and Klein (2020) put forth.
Data feminism views data as situated and partial, drawing on intersectional feminism’s view ofknowledge as particular to a specific time, place,and people (Harding, 1995; Crenshaw, 1991; Haraway, 1988). Feminist theories argue that the standpoint (perspective) of a person impacts knowledge
and understanding, and that a universal standpoint
cannot exist (Harding, 1995; Haraway, 1988). Indigenous epistemologies, such as the Lakota concept of waȟkàN, further the notion of the impossibility of a universal truth (Lewis et al., 2018). Translated as “that which cannot be understood,” waȟkàNcommunicates that knowledge may come from aplace beyond what we are capable of imagining(ibid.). To create a dataset of GLAM documentation annotated for gender biased language, thisproject creates an annotation taxonomy that allows
for gender information to be labeled as uncertain
or excluded, and incorporates multiple annotators’
perspectives in the model training data.
To practically apply theories and approaches
from perspectivist NLP, data feminism, and indigenous epistemologies, the project applies the casestudy method common to social sciences and design research. Case studies use a combination ofdata and information gathering approaches to study
particular phenomena in context, focusing on “consideration of the whole, covering interrelationships,”
which provides a “depth [that] compensates for any
shortcomings in breadth and the ability to generalize” (Martin and Hanington, 2012, 28). Furthermore, case studies report and reflect upon outliers
discovered in the research process (ibid.), useful forthis project’s effort to create space for the perspectives of minoritized people. This project provides
a case study for NLP bias research with the longterm aim of building a collection of case studies,
which would enable the NLP community to determine the aspects of bias mitigation approaches that
can and cannot be generalized across contexts.

3

Methodology

The interdisciplinary nature of the Ph.D. project
warrants a combination of methods and frameworks
from several disciplines. Adopting the bias-aware
methodology of Havens et al. (2020), case study
and participatory action research methods comple-

ment NLP methods for creating the project’s annotation taxonomy, annotated datasets, and classification models. Critical discourse analysis, feminist
theories, queer theory, and indigenous epistemologies provide frameworks through which to analyze
the project’s metadata descriptions and annotateddatasets. To begin, the author defined gender bias
using the definition of biased language of Havens
et al. (2020) (quoted in §1) narrowed to gender bias.
This definition informs the annotation taxonomy,
which in turn will influence classifiers created withthe annotated data.
Participatory action research methods are used
to incorporate stakeholder perspectives, necessary
for situating a study of gender bias in a particular time, place, and people. Situated in the United
Kingdom, the project works with archival documentation written in British English from the Centre
for Research Collections at the University of Edinburgh (CRC).2 Due to the numerous characteristics
on which bias may be based, such as racialized
ethnicities, economic class, gender, and sexuality,
a focus on gender bias was chosen. This focus supports the CRC’s existing effort to mitigate genderbias in its collections. A person’s gender is considered to be self-described, changeable, and capableof falling anywhere along a spectrum of femininity
to masculinity (Scheuerman et al., 2020; Keyes,2018). Archivists provided feedback during thedevelopment of the project’s annotation taxonomy(§A.8), and will provide feedback in future work
analyzing the data annotated with the taxonomy.
The annotation taxonomy and instructions for
applying the taxonomy focus on documenting information explicit in the text to avoid misgendering(Scheuerman et al., 2020). The annotations do notinfer a person’s gender from the person’s name,occupation, or other descriptive information, nor
do the annotations assign a particular gender toa person. Rather, the annotation process records
whether the terms used to describe a person are“feminine,” “non-binary,” “masculine,” or, if only
gender-neutral terms are used, “unknown.” Annotators were instructed to read the metadata descriptions from their contemporary perspective. Thatbeing said, as the historian Shopland writes, “whenwriting of historic LGBTQIA+ people, we use adefinition which simply did not exist in their lifetimes” (2020, 1). Consequently, the project acknowledges that the perspectives documented in
2archives.collections.ed.ac.uk

the annotation process are situated not only geographically and culturally, but also temporally, inthe 21st century.
Following Smith’s (2006) approach, the project
views heritage as a process. Smith writes, “whatmakes certain activities ‘heritage’ are those activities that actively engage with thinking about andacting out not only ‘where we have come from’ interms of the past, but also ‘where we are going’ interms of the present and future” (ibid., 84). The
annotation process of this project visits, interprets,
and negotiates with heritage (ibid.) in the formof archival documentation, directing NLP technology towards trans-inclusive conceptualizations ofgender, and making gender biases in archival documentation transparent. Smith’s approach to heritage draws on Fairclough’s (2003) approach to
critical discourse analysis (CDA).
Discourse consists of language and its production, interpretation, and social context (Fairclough,2003; Marston, 2000). CDA thus provides a valuable lens through which to study the heritage material of this project: descriptions from an archival
catalog. Considering language in its context ofuse, CDA offers an approach to studying how language legitimizes, maintains, and challenges power(Smith, 2006; Fairclough, 2003; Marston, 2000).
The project uses CDA to follow the data feminism
principles of examining and challenging power(D’Ignazio and Klein, 2020). Through annotationsof gender biased language, the project examines
and challenges the dominant perspective of men
in the archival metadata descriptions, making thisperspective explicit and identifying opportunities
for perspectives of additional genders to be incorporated into the descriptions.

4Work Achieved

The project has accomplished O1, defining andcategorizing types of gender biased language forarchives, through the creation of an annotation taxonomy. The taxonomy defines types of gender bias
to label in a corpus of archival documentation. Currently the project is progressing on O2 and O3,
which are interrelated: the manual annotation process allowed for calculations of the prevalence ofgender biased language on a subset of archival documentation, and the classifiers, once built, will enable more complete calculations of gender biased
language on the remainder of the descriptions in
the archive’s catalog. This section summarizes theCount
Words
Sentences

Title

Biographical/Historical

Scope & Contents

Processing Information

Total

4,834
51,904
5,932

576
75,032
3,829

6198
269,892
14,412

280
3,129
301

11,888399,957
24,474

Table 1: Total counts, words and sentences for descriptive metadata fields in the aggregated dataset. Calculations
were made using Punkt tokenizers in the Natural Language Toolkit Python library (Loper and Bird, 2002).

work achieved on O1, O2, and O3; the next section(§5) outlines potential directions for completingO3. Havens et al. (2022) contains a detailed discussion of the annotation taxonomy and its application
to create an annotated dataset.
The project’s annotation taxonomy builds onliterature from ML (Hitti et al., 2019), humancomputer interaction (Keyes, 2018; Scheuerman
et al., 2020), gender studies (Butler, 1990), archival
science (Tanselle, 2002), and linguistics (Fairclough, 2003; Bucholtz, 2003, 1999). Group interviews and workshops (participatory action researchmethods (Moore, 2018; Swantz, 2008; Reid andFrisby, 2008)) further informed the annotation taxonomy. The final annotation taxonomy consists ofthree categories. Each category contains subcategories with the labels that the annotators applied toarchival metadata descriptions. §A.8 contains thecomplete taxonomy with definitions and examples.
The first two categories of labels, Person Nameand Linguistic, annotate vocabulary choices andlexical relations that are explicit in the descriptions,providing a record of the “internal’ relations oftexts” (Fairclough, 2003, 36-7). The third categoryof labels, Contextual, annotates according to thedescriptions’ relationship with a social context (forexample, events, behaviors, and power structures),providing a record of the “‘external’ relations oftexts” (Fairclough, 2003, 36). Approaching thearchival documentation as discourse, the annotations make the connections between the internaland external relations of language transparent.
Annotating heritage in the form of archival metadata descriptions adds to the process that is heritage, evolving the meaning of the descriptions(Smith, 2006). Applying annotations to archival
metadata descriptions from a 21st century perspective recontextualizes the descriptions, adding to thegenre chain, or network, of archival documentation that begins with the archival items and continued with catalogers’ descriptions of the items(Fairclough, 2003). The taxonomy permits anno-tators to record uncertainty and absence of information (Shopland, 2020; Lewis et al., 2018), deviating from past NLP documentation approaches(i.e., Garnerin et al. (2020); Dinan et al. (2020)).3
Participatory action research found that archivists
view archival documentation as incomplete. The
primary purpose of describing archival items is toenable their discoverability, but this must be balanced with the need to describe a backlog of newarchival items perpetually being acquired.
The corpus of archival documentation for annotation were created by harvesting metadata descriptions from an online catalog, reformatting thedescriptions for annotation, and manually labelingthe descriptions according to the annotation taxonomy. The archival documentation comes from four
metadata descriptions in the online archival catalogof the CRC: Title, Biographical / Historical, Scope
and Contents, and Processing Information. Though
not all descriptions have a date recording when they
were written, the earliest recorded date of a description’s writing is 1896 and the latest, 2020. The
CRC’s Archives include a variety of material, suchas photographs, letters, manuscripts (letters, lecture notes, and other handwritten documents), andinstruments; and cover a range of topics, including
town planning, research and teaching, and Scottish
Presbyterianism. The language of the Archives’materials are mostly English (1,018 out of 1,315
collections, about 77%), though over 80 languages
total are present across the collections. The descriptions that were annotated account for about
20% of the text in the entire online catalog of the
CRC’s Archives. Table 1 provides summary statistics about the data. For further detail on the size,
contents, and organization of the annotation corpus,
please refer to the paper by Havens et al. (2022) andthe data statement (Bender and Friedman, 2018) inAppendix A.9.
The project received grants to hire four anno3
Domains beyond GLAM also face the challenge of uncertain and absent information (Andrus et al., 2021).

tators, who were Ph.D. students selected for theirexperience in gender studies or archives. The total
cost of the annotation work amounted to circa 400
hours of work and £5,333.76. The four hired annotators each worked 72 hours over eight weeks, receiving £18.52 per hour. The author spent 86 hours
annotating over 16 weeks for her Ph.D. project.
Though all annotators identify as women, due tothe historical dominance of men’s perspective in
the English language and the pejoration of terms
describing women (Spencer, 2000; Schulz, 2000;
Lakoff, 1989),4 the project’s annotated dataset doeschallenge dominant perspectives in archival discourse to advance gender equity (D’Ignazio andKlein, 2020; Fairclough, 2003).
Inter-annotator agreement (IAA) calculations reflect the subjectivity of gender bias (see §B, tables
2, 3, 4, and 5). Annotating gendered language
proved to be more straightforward than annotating
gender biased language. We report IAA with F1
as our metric due to the limitations of coefficients’assumptions and interpretability as Artstein andPoesio (2008) discuss. F1 scores for the genderedlanguage labels “Gendered Role” and “Gendered
Pronoun” fall between 0.71 and 0.99. F1 scores forannotating gender biased language are relatively
low, with the greatest agreement on the “Generalization” label at only 0.56, on the “Omission” label
at 0.48, and on the “Stereotype” label at 0.57. Manual analysis of disagreements among annotators
demonstrated the value of a perspectivist approachto disagreements (Davani et al., 2022; Sang andStanton, 2022; Basile et al., 2021), as multiple annotators’ labels were often deemed correct for thesame text span.
The five annotators’ datasets were merged into
one aggregated dataset, which will be divided intotraining, development, and test sets for creatingclassifiers. Aggregation began with a one-hour
manual review of each annotator’s labels to identify
patterns and common mistakes, which informed
the subsequent aggregation steps. Disagreeing labels for the same text span were then manuallyreviewed, with either a combination or an individual label being chosen for each text span to include
in the aggregated dataset.
Next, for annotations with overlapping text spans
and the same label (considered to be in agreement),
the annotation with the largest text span was added
4I.e., in the 16th century, grammarians instructed that man
precede woman in writing; in the 18th century, man and hebegan to be used in place of human and their (Spencer, 2000).

to the aggregated dataset. All remaining annotations were then added to the aggregated dataset,with the exception of one annotator’s Person Namelabels, as these were applied with great inconsistency relative to other annotators. §A.1 details thereview and aggregation of the annotated datasets.
Figures 2 illustrates the prevalence of each of thetaxonomy’s labels in the aggregated dataset andFigure 3 illustrates how many annotations fromeach annotator are in the aggregated dataset. The
annotated datasets are a starting point to identify
gender bias in GLAM documentation in the UK;
they are not intended to comprehensively cover of
all gender biases that may come through in GLAM
documentation. They will serve as training, development, and test data for developing classifiers,
and will be published alongside the classifiers infuture work.

5

Discussion and Conclusion

Now passing the halfway point of a Ph.D. degree, with a year and six months remaining, theproject would benefit from feedback on possible
approaches to the project’s last objectives. Several
approaches are under consideration for buildingclassification models (O3).
Four algorithms are under consideration forbuilding a gender biased document classifier: (1)logistic regression (LR), as a classification baseline (Jurafsky and Martin, 2000); (2) decision treeor (3) random forest (a combination of randomized decision trees), as the decision trees are themost transparent algorithm for classification (Birdet al., 2019); and (4) support vector machines, as
Adhikari et al. (2019) found this outperformed LR
and neural models on document classification for
select datasets. The document classifiers could be
developed as single task or multitask; the project
would like to investigate correlations between labels. As the perspectivist approach to disagreements in NLP encourages (Basile, 2022), classifiers
could be trained on individual annotators’ datasetsin addition to the aggregated dataset. Documentclassifiers could also be pre-trained on a deep learning model such as DocBERT (Adhikari et al., 2020)
to see if pre-training improves their performance.
The focus on document classification comesfrom the intended use case of the classification
models: to support archivists in identifying descriptions with gender biases in their catalogs. Such
identification would support the efficient prioriti-

Total Annotations Per Label in the Aggregated Dataset
ANNOTATION LABEL

35000

Generalization

Feminine

Gendered Role

Unknown

Gendered Pronoun

Occupation

15000

Masculine

Omission

10000

Non-binary

Stereotype

ANNOTATION COUNT

30000
25000
20000

Empowering

50000Linguistic

Person Name

Contextual

ANNOTATION CATEGORY

Figure 1: A stacked bar chart of counts of annotations per label across all annotators in the aggregated dataset of
55,260 total annotations, organized into the three categories of labels: Linguistic, Person Name, and Contextual.
“Non-binary” (a Person Name label) and “Empowering” (a Contextual label) both have a count of zero.

Total Annotations Per Annotator in the Aggregated Dataset
Annotator 0

ANNOTATION CATEGORIESAllANNOTATOR

Annotator 1

Person Name andLinguistic

Annotator 2
Annotator 3

Contextual
Annotator 4
0

5000

10000 15000 20000 25000 30000 35000
ANNOTATION COUNT

Figure 2: A bar chart of the total annotations from each annotator included in the aggregated dataset, with colors
indicating the category of labels each annotator used. For annotations that matched or overlapped, only one wasadded to the aggregated dataset, so the total number of annotations in the aggregated dataset (55,260) is 21,283 lessthan the sum of the annotators’ annotations in this chart (76,543).

zation of reparative description practices that add
context to or reword harmful descriptions. That being said, annotators applied labels to text spans, notdocuments, so sentence classification could also be
pursued. Though all approaches have the potential
to contribute to NLP and GLAM’s efforts to mitigate bias, the 18 months remaining in the Ph.D.
provides only enough time for select approaches tobe pursued. The project would appreciate feedback
at the Student Research Workshop on approaches
under consideration to build and evaluate classifiers
that detect gender biased documents.

Acknowledgements
Thank you to the organizers of the Student Research Workshop at NAACL-HLT 2022 for theopportunity to submit this in-progress work, and to
the reviewers of this paper for their feedback. Additional thanks go to my Ph.D. supervisory team,
Bea Alex, Ben Bach, and Melissa Terras; as wellas Rachel Hosker and her team at the CRC for
their enthusiastic collaboration. Thank you also to
the organizations who have funded this Ph.D.: theUK’s Engineering and Physical Sciences ResearchCouncil and the University of Edinburgh’s Schoolof Informatics Graduate School. The annotation

work in this project was made possible by generous
funding from the Institute for Language, Cognition, and Computation in the School of Informatics
and the Edinburgh Futures Institute, both at the
University of Edinburgh.

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A

Data Statement: Annotated Datasets ofArchival Documentation

A.1

Curation Rationale

These datasets were created from a corpus of 1,460
files of archival metadata descriptions totaling circa
15,419 sentences and 255,943 words. That corpus
is the first 20% of text from the corpus describedin the Provenance Appendix (§A.9), annotated forgender bias according the the taxonomy in Other(§A.8). 73 of files (10% of the text) were triply annotated; the remaining 1,387 files (90% of the text)
were doubly annotated. There are six instances of
the annotated corpus: one for each of the five annotators and one that aggregates all annotators’ labels.
Participatory action research with archivists led the
project to choose four metadata fields were chosen
in the archival catalog to extract for annotation: Title, Scope and Contents, Biographical / Historical,
and Processing Information.
The five annotated datasets were merged intoa single aggregated dataset for classifier training
and evaluation, so comparisons could be made onclassifiers’ performances after training on an individual annotator’s dataset versus on the aggregateddataset. The merging process began with a onehour manual review of each annotator’s labels to
identify patterns and common mistakes in their labeling, which informed the subsequent steps for
merging the five annotated datasets.
The second step of the merging process was tomanually review disagreeing labels for the sametext span and add the correct label to the aggregated dataset. Disagreeing labels for the same text
span were reviewed for all Person Name, Linguistic, and Contextual categories of labels. For Person
Name and Linguistic labels, where three annotatorslabeled the same span of text, majority voting determined the correct label: if two out of the three
annotators used one label and the other annotator
used a different label, the label used by the twoannotators was deemed correct and added to theaggregated dataset. For Contextual labels, unless
an obvious mistake was made, the union of all three
annotators’ labels was included in the aggregateddataset.
Thirdly, the “Occupation” and “Gendered Pronoun” labels were reviewed. A unique list of thetext spans with these labels was generated and incorrect text spans were removed from this list. The“Occupation” and “Gendered Pronoun” labels in
the annotated datasets with text spans in the uniquelists of valid text spans were added to the aggregated dataset. Fourthly, the remaining Linguisticlabels (“Gendered Pronoun,” “Gendered Role,” and“Generalization”) not deemed incorrect in the annotated datasets were added to the aggregated dataset.
Due to common mistakes in annotating Person
Name labels with one annotator, only data from theother two annotators who annotated with Person
Name labels was added to the aggregated dataset.
Fifthly, for annotations with overlapping text spans
and the same label, the annotation with the longer
text span was added to the aggregated dataset. The
sixth and final step to constructing the aggregateddataset was to take the union of the remaining Contextual labels (“Stereotype,” “Omission,” “Occupation,” and “Empowering”) not deemed incorrect inthe three annotated datasets with these labels andadd them to the aggregated dataset.
A.2

Language Variety

The metadata descriptions extracted from theArchive’s catalog are written primarily in British
English, with the occasional word in another language such as French or Latin.
A.3

Producer Demographic

The producing research team are of American, German, and Scots nationalities, and are three womenand one man. We all work primarily as academic
researchers in the disciplines of natural language
processing, data science, data visualization, humancomputer interaction, digital humanities, and digital cultural heritage. Additionally, one of us is
audited an online course on feminist and social
justice studies.
A.4

Annotator Demographic

The five annotators are of American and European
nationalities and identify as women. Four annotators were hired by the lead annotator for theirexperience in gender studies and archives. The
four annotators worked 72 hours each over eightweeks in 2022, receiving £1,333.44 each (£18.52
per hour). The lead annotator completed the workfor her Ph.D. project, which totaled to 86 hours ofwork over 16 weeks.
A.5

Speech or Publication Situation

The archival metadata descriptions describe material about a range of topics, such as teaching, research, town planning, music, and religion. The materials described also vary, from letters and journals
to photographs and audio recordings. The descriptions in this project’s dataset with a known date
(which describe 38.5% of the archives’ records)
were written from 1896 through 2020.
The annotated dataset will be published with aforthcoming paper detailing the methodology andtheoretical framework that guided the development

of the annotation taxonomy and the annotation process, accompanied by analysis of patterns and outliers in the annotated dataset.
A.6

Data Characteristics

The datasets were organized for annotation in a
web-based annotation paltform, the brat rapid annotation tool (Pontus Stenetorp, Sampo Pyysalo,
Goran Topić, Tomoko Ohta, Sophia Ananiadou,
Jun’ichi Tsujii, 2012). Consequently, the data formats conform to the brat formats: plain text files
that end in ‘.txt’ contain the original text and plain
text files that end in ‘.ann’ contain the annotations.
The annotation files include the starting and ending
text span of a label, the actual text contained inthat span, the label name, and any notes annotators recorded about the rationale for applying thelabel they did. The names of all the files consistof the name of the fonds (the archival term for acollection) and a number indicating the starting line
number of the descriptions. Descriptions from asingle fonds were split across files so that no filecontained more than 100 lines, because brat could
not handle the extensive length of certian fonds’
descriptions.
A.7

Data Quality

A subset of annotations were applied automaticallywith a grep script and then corrected during themanual annotation process. All three categories of
the annotation taxonomy were manually applied by
the annotators. The lead annotator then manuallychecked the labels for accuracy. That being said,
due to time constraints, mistakes are likely to remain in the application of labels (for example, thestarting letter may be missing from a labeled text
span or a punctuation mark may have accidentally
been included in a labeled text span).
A.8

Other: Annotation Schema

The detailed schema that guided the annotation process is listed below with examples for each label.
In each example, the labeled text is underlined. All
examples are taken from the dataset except for labels 1.1, “Non-binary,” and 3.4, “Empowering,” asthe annotators did not find any text to which theprovided label definitions applied. The annotation
instructions permitted labels to overlap as each annotator saw fit, and asked annotators to read andannotate from their contemporary perspective. The
categories of labels from the annotation taxonomy

were divided among annotators: two hired annotators labeled with categories 1 and 2, two hired
annotators labeled with category 3, and the leadannotator labeled with all categories.
The annotation taxonomy includes labels for gendered language, rather than only explicitly genderbiased language, because measuring the use of gendered words across an entire archives’ collection
provides information about gender bias at the overall collections’ level. For example, using a gendered pronoun such as “he” is not inherently biased,
but if the use of this masculine gendered pronoun
far outnumbers the use of other gendered pronouns
in our dataset, we can observe that the masculine
is over-represented, indicating a masculine bias in
the archives’ collections overall. Labeling genderbiased language focuses on the individual description level. For example, the stereotype of a wife
playing only or primarily a supporting role to herhusband comes through in the following description:
Jewel took an active interest in her husband’s work, accompanying him when hetravelled, sitting on charitable committees, looking after missionary furlough
houses and much more. She also wrote
a preface to his Baptism and Conversion
and a foreward [sic] to his A Reasoned
Faith.. (Fonds Identifier: Coll-1036)
1. Person Name: the name of a person, including any pre-nominal titles (i.e., Professor, Mrs., Sir, Queen), when the person is theprimary entity being described (rather than alocation named after a person, for example)
1.1 Non-binary:* the pronouns or rolesof the named person within the descriptive
field in which this instance of the name appears (either Title, Scope and Contents, Biographical / Historical, or Processing Information) are non-binary
Example 1.1: Francis McDonald went
to the University of Edinburgh where theystudied law.
Note: the annotation process did not find suitable text on which to apply this label in thedataset.
1.2 Feminine: the pronouns, titles, orroles of the named person within the descriptive field in which this instance of the nameappears (either Title, Scope and Contents, Biographical / Historical, or Processing Information) are feminine
Example 1.2: “Jewel took an active
interest in her husband’s work...” (Fonds Identifier: Coll-1036)
1.3 Masculine: the pronouns, titles, orroles of the named person within the descriptive field in which this instance of the nameappears (either Title, Scope and Contents, Biographical / Historical, or Processing Information) are masculine
Example 1.3: “Martin Luther, the man
and his work.” (Fonds Identifier: BAI)
1.4 Unknown: any pronouns, titles, orroles of the named person within the descriptive field in which this instance of the nameappears (either Title, Scope and Contents, Biographical / Historical, or Processing Information) are gender neutral, or no such pronouns
or roles are provided within the descriptive
field
Example 1.4: “Testimonials and additional testimonials in favour of Niecks, candidacy for the Chair of Music, 1891” (Fonds
Identifier: Coll-1086)
2. Linguistic: gender marked in the way a word,phrase or sentence references a person or people, assigning them a specific gender that does
not account for all genders possible for that
person or people
2.1 Generalization: use of a genderspecific term (i.e. roles, titles) to refer to agroup of people that could identify as morethan the specified gender
Example 2.1: “His classes included
Anatomy, Practical Anatomy, ... Midwifery
and Diseases of Women, Therapeutics, Neurology, ... Public Health, and Diseases of the
Skin.” (Fonds Identifier: Coll-1118)

2.3 Gendered Pronouns: explicitly
marking the gender of a person or people
through the use of pronouns (e.g., he, him,
himself, his, her, herself, and she)
Example 2.3: “He obtained surgicalqualifications from Edinburgh University in1873 ([M.B.]).” (Fonds Identifier: Coll-1096)
3. Contextual: expectations about a gender orgenders that comes from knowledge about
the time and place in which language is used,rather than from linguistic patterns alone (i.e.,
sentence structure or word choice)
3.1 Stereotype: a word, phrase, or sentence that communicates an expectation of aperson or group of people’s behaviors or preferences that does not reflect the reality of all
their possible behaviors or preferences; or aword, phrase, or sentence that focuses on aparticular aspect of a person that doesn’t represent that person holistically
Example 3.1: “The engraving depicts
a walking figure (female) set against sunlight,
and holding/releasing a bird.” (Fonds Identifier: Coll-1116)
3.2 Omission: focusing on the presence,
responsibility, or contribution of a single gender in a situation in which more than one gender has a presence, responsibility or contribution; or defining one person’s identity in termsof their relation to another person
Example 3.2: “This group portraitof Laurencin, Apollinaire, and Picasso andhis mistress became the theme of a larger version in 1909 entitledApollinaire [sic] and his
friends.” (Fonds Identifier: Coll-1090).
3.3 Occupation: a word or phrase thatrefers to a person or people’s job title (singular or plural) for which the person or people
received payment; do not annotate occupations used as a pre-nominal title (for example, “Colonel Sir Thomas Francis Fremantle”
should not have an occupation label)

2.2 Gendered Roles: use of a title orword denoting a person’s role that marks either a non-binary, feminine, or masculine gender

Example 3.3: “He became a surgeon
with the Indian Medical Service.” (Fonds
Identifier: Coll-1096).

Example 2.2: “New map of Scotland
for Ladies Needlework, 1797” (Fonds Identifier: Coll-1111)

3.4 Empowering: reclaiming derogatory
words or phrases to empower a minoritized
person or peopleExample 3.4: a person describing
themself as queer in a self-affirming, positive
mannerNote: the annotation process did not findenough text on which to apply this label inthe dataset to include it when training a classifier.
*The “Non-binary” label was not used by the annotators. That being said, this does not mean there
were not people who would identify as non-binary
represented in the text of the annotation corpus.
When relying only on descriptions written by people other than those represented in the descriptions,knowledge about people’s gender identity remains
incomplete (Shopland, 2020). Additional linguistic
research informed by a knowledge of terminology
for the relevant time period may identify people
who were likely to identify as non-binary in the corpus of archival metadata descriptions. For example,
Shopland (2020) finds that focusing on actions thatpeople were described doing can help to locate people of minoritized genders (and sexualities) in historical texts, but also cautions researchers against
assuming too much. A full understanding of a person’s gender often remains unattainable from the
documentation that exists about them.
A.9

Provenance Appendix

Data Statement: Corpus of Archival Documentation
A.9.1

Curation Rationale

We (the research team) will use the extracted metadata descriptions to create a gold standard dataset
annotated for contextual gender bias. We adopt
Hitti et al.’s definition of contextual gender bias intext: written language that connotes or implies an
inclination or prejudice against a gender throughthe use of gender-marked keywords and their context (2019, p. 10-11).
A member of our research team has extracted
text from four descriptive metadata fields for all collections, subcollections, and items in the Archive’s
online catalog. The first field is a title field. The
second field provides information about the people, time period, and places associated with thecollection, subcollection, or item to which the field
belongs. The third field summarizes the contentsof the collection, subcollection, or item to which
the field belongs. The last field records the person
who wrote the text for the collection, subcollection,or item’s descriptive metadata fields, and the date
the person wrote the text (although not all of thisinformation is available in each description; someare empty).
Using the dataset of extracted text, we will experiment with training a discriminative classification
algorithm to identify types of contextual genderbias. Additionally, the dataset will serve as a sourceof annotated, historical text to complement datasetscomposed of contemporary texts (i.e. from social
media, Wikipedia, news articles).
We chose to use archival metadata descriptions asa data source because:
1. Metadata descriptions in the Archive’s catalog (and most GLAM catalogs) are freely,
publicly available online
2. GLAM metadata descriptions have yet to beanalyzed at large scale using natural language
processing (NLP) methods and, as recordsof cultural heritage, the descriptions havethe potential to provide historical insights
on changes in language and society (Welsh,
2016)
3. GLAM metadata standards are freely, publicly
available, often online, meaning we can use
historical changes in metadata standards usedin the Archive to guide large-scale text analysis of changes in the language of the metadata
descriptions over time
4. The Archive’s policy acknowledges its responsibility to address legacy descriptions in its
catalogs that use language considered biased
or otherwise inappropriate today5
A.9.2

Language Variety

The metadata descriptions extracted from theArchive’s catalog are written in British English.
A.9.3

Producer Demographic

We (the research team) are of American, German,
and Scots nationalities, and are three females andone male. We all work primarily as academic researchers in the disciplines of natural language processing, data science, data visualization, human5
The Archive is not alone; across the GLAM sector, institutions acknowledge and are exploring ways
to address legacy language in their catalogs’ descriptions.
The “Note” in We Are What We Steal provides one example: https://dxlab.sl.nsw.gov.
au/we-are-what-we-steal/notes/.

computer interaction, digital humanities, and digital cultural heritage. Additionally, one of us has
been auditing a feminism and social justice course,
and reading literature on feminist theories, queer
theory, and indigenous epistemologies.
A.9.4 Annotator Demographic
Not applicable
A.9.5 Speech or Publication Situation
The metadata descriptions extracted from theArchive’s online catalog using Open Access Initiative - Protocol for Metadata Harvesting (OAIPMH). For OAI-PMH, an institution (in this case,
the Archive) provides a URL to its catalog that
displays its catalog metadata in XML format. A
member of our research team wrote scripts in
Python to extract three descriptive metadata fields
for every collection, subcollection, and item in theArchive’s online catalog (the metadata is organizedhierarchically). Using Python and its Natural Language Toolkit library (Loper and Bird, 2002), theresearcher removed duplicate sentences and calculated that the extracted metadata descriptions
consist of a total of 966,763 words and 68,448
sentences across 1,231 collections. The minimum
number of words in a collection is 7 and the maximum, 156,747, with an average of 1,306 words percollection and standard deviation of 7,784 words.
The archival items described in resulting corpus
consist of a variety of material, from photographs
and manuscripts (letters, lecture notes, and other
handwritten documents) to instruments and tweets.
A.9.6 Data Characteristics
Upon extracting the metadata descriptions using
OAI-PMH, the XML tags were removed so thatthe total words and sentences of the metadata descriptions could be calculated to ensure the text
source provided a sufficiently large dataset. A
member of our research team has grouped all theextracted metadata descriptions by their collection(the “fonds” level in the XML data), preserving thecontext in which the metadata descriptions were
written and will be read by visitors to the Archive’s
online catalog.
A.9.7 Data Quality
As a member of our research team extracts andfilters metadata descriptions from the Archive’s
online catalog, they write assertions and tests toensure as best as possible that metadata is not lostor unintentionally changed.

A.9.8 Other
Not applicable
A.9.9 Provenance Appendix
Not applicableB

Inter-Annotator Agreement

The following pages display four tables of interannotator agreement (IAA) scores: among annotators, table 2 for the Person Name and Linguistic
categories of labels, and table 3 for the Contextual
category of labels; annotators versus the aggregateddataset, table 4 for the Person Name and Linguistic
categories of labels, and table 5 for the Contextual
category of labels. IAA was calculated such thatoverlapping text spans with the same label wereconsidered to be in agreement, in addition to matching text spans with the same label. Due to the aimof training document classification models on the
annotated datasets, the existence of a particular
type of gender bias in an archival metadata description was deemed more important than the precise
words that communicate gender bias.

exp

pred

label

true
pos

false
pos

false
neg

precision

recall

F1

files

0
0
1
0
0
1
0
0
1
0
0
1
0
0
1
0
0
1
0
0
1

1
2
2
1
2
2
1
2
2
1
2
2
1
2
2
1
2
2
1
2
2

Unknown
Unknown
Unknown
Masculine
Masculine
Masculine
Feminine
Feminine
Feminine
Non-binary
Non-binary
Non-binary
Gendered Role
Gendered Role
Gendered Role
Gendered Pronoun
Gendered Pronoun
Gendered Pronoun
Generalization
Generalization
Generalization

5031
2776
1048
2367
728
380
627
724
287
0
0
0
1802
1404
438
3398
869
518
37
74
2

1524
537
1421
2372
111
169
427
128
496
0
0
0
306
162
292
101
70
7
35
51
50

4268
432
315
1079
146
411
642
178
279
0
0
0
882
257
52
190
60
11
262
63
7

0.767510.83791
0.424460.499470.867700.692170.594880.84977
0.366540.854840.896550.60000
0.971130.925450.98667
0.51389
0.59200
0.03846

0.54103
0.86534
0.76889
0.68688
0.83295
0.480400.494090.80266
0.507070.671390.84527
0.893880.94705
0.935410.979210.123750.540150.222220.63467
0.851400.54697
0.57838
0.84997
0.567160.539820.825540.425500.752090.870160.71803
0.958940.930410.98292
0.19946
0.564890.06557

584
170
72
584
170
72
584
170
72
584
170
72
584
170
72
584
170
72
584
170
72

Table 2: Inter-annotator agreement (IAA) measures for annotators who used the Person Name and Linguistic
categories of labels to annotate archival documentation. The first two columns note the annotator whose labels wereconsidered expected or predicted, respectively. The abbreviation “pos” is for “positive;” “neg,” for “negative.” Thelast column lists the number of files with annotations by both annotators for that row. No annotators applied the“Non-binary” label.

exp

pred

label

true
pos

false
pos

false
neg

precision

recall

F1

files

0
0
3
0
0
3
0
0
3
0
0
3

3
4
4
3
4
4
3
4
4
3
4
4

Occupation
Occupation
Occupation
Omission
Omission
Omission
Stereotype
Stereotype
Stereotype
Empowering
Empowering
Empowering

1988
738
422
1376
416
215
505
507
34
0
0
0

613
396
327
914
317
315
539
525
60
80
0
0

724
240
134
3259
875
155
227
600
161
0
0
80

0.764320.65079
0.563410.60087
0.56753
0.40566
0.48371
0.49127
0.36170-0.733030.75460
0.75899
0.29687
0.32223
0.581080.689890.457990.17435-

0.74835
0.69886
0.64674
0.397400.41106
0.47777
0.56869
0.47405
0.23529
-

485
149
57
485
149
57
485
149
57
485
149
57

Table 3: Inter-annotator agreement (IAA) measures for annotators who used the Contextual category of labels toannotate archival metadata descriptions. The first two columns note the annotator whose labels were considered
expected or predicted, respectively. The abbreviation “pos” is for “positive;” “neg,” for “negative.” The last column
lists the number of files with annotations by both annotators for that row. Only annotator 3 applied the “Empowering”
label.

exp

pred

label

true
pos

false
pos

false
neg

precision

recall

F1

files

Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg

0
1
2
0
1
2
0
1
2
0
1
2
0
1
2
0
1
2
0
1
2

Unknown
Unknown
Unknown
Masculine
Masculine
Masculine
Feminine
Feminine
Feminine
NonbinaryNonbinaryNonbinary
Gendered-Role
Gendered-Role
Gendered-Role
Gendered-Pronoun
Gendered-Pronoun
Gendered-Pronoun
Generalization
Generalization
Generalization

10561
6608
15140
3963
4749
1007
1454
1076
994
0
0
0
3108
1924
1471
3933
3498
1016
405
69
127

36
0
117
18
1
5
19
0
12
0
0
0
697
218
652
160
3
1
1
4
0

1900
4511
679
2446
1099
525
523
707
410
0
0
0
330
716
230
165
190
41
1370
1123
862

0.99660
1.00000
0.99233
0.995480.99979
0.99506
0.987101.00000
0.98807
0.81682
0.89823
0.692890.960910.999140.99902
0.997540.945211.00000

0.84752
0.59430
0.95708
0.61835
0.812070.657310.73546
0.603480.70798
0.90401
0.72879
0.86479
0.95974
0.948480.961210.228170.05789
0.128410.916040.745530.974390.762850.896210.79167
0.842900.752710.824900.858210.80468
0.769350.96032
0.973150.97975
0.371390.10909
0.22760

714
597
444
714
597
444
714
597
444
714
597
444
714
597
444
714
597
444
714
597
444

Table 4: Inter-annotator agreement (IAA) between the aggregated dataset and annotators for the Person Name andLinguistic categories of labels to annotate archival documentation. The first two columns note the annotator whoselabels were considered expected or predicted, respectively. The abbreviation “pos” is for “positive;” “neg,” for“negative.” The last column lists the number of files with annotations by both annotators for that row. No annotators
applied the “Non-binary” label.

exp

pred

label

true
pos

false
pos

false
neg

precision

recall

F1

files

Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg
Agg

0
3
4
0
3
4
0
3
4
0
3
4

Occupation
Occupation
Occupation
Omission
Omission
Omission
Stereotype
Stereotype
Stereotype
Empowering
Empowering
Empowering

2725
2320
1746
5916
2310
1876
1748
1089
1400
0
0
0

23
290
147
12
13
5
11
9
2
0
80
0

571
873
253
1187
3475
967
1058
279
715
0
0
0

0.991630.88889
0.92235
0.99798
0.994400.99734
0.99375
0.99180
0.99857
0.0-

0.82676
0.72659
0.873440.83289
0.399310.65987
0.62295
0.79605
0.66194-

0.90172
0.79959
0.897230.907990.569810.794240.76583
0.883210.79613
0.0
-

631
508
450
631
508
450
631
508
450
631
508
450

Table 5: Inter-annotator agreement (IAA) between the aggregated dataset and annotators for the Contextual categoryof labels to annotate archival metadata descriptions. The first two columns note the annotator whose labels wereconsidered expected or predicted, respectively. The abbreviation “pos” is for “positive;” “neg,” for “negative.” Thelast column lists the number of files with annotations by both annotators for that row. Only annotator 3 applied the“Empowering” label.