dataesr/scientific-paragraphs-categorization huggingface dataset:
In doing so, we help the extant IS theory as well as practice to take a small step away from the traditional "what" questions and go towards the more insightful "how" ones, while we contribute to the line of IS research on theorising using large datasets, and novel computational approaches  (Kar & Dwivedi, 2020). The rest of the paper is organized as follows: in the next section, we present the analytical framework of our study. This is followed by a description of our methodology, where we present our data collection and analysis. The penultimate section of the paper presents a discussion of our findings and details the implications of our study for the broader IS theory and practice. We conclude the paper by delineating an agenda for future research on the topic.
Data We extracted all academic outputs related to "consumer" and "identity" from five databases (ABI Inform, EBSCO Business Premier, Psychinfo, Sociological Abstracts, and Web of Science). A total of 13,769 academic outputs were initially obtained, but after removing duplicates, non-English articles, letters to the editor, book reviews, and articles not specifically focused on consumer identity (e. g. , consumer identity theft), the total number of academic outputs in our dataset was down to 3,328. Then, we used the Scimago Journal List to categorize the journals by field. When two or more fields were listed for a journal, we selected the one with the highest quartile ranking for the longest period. To maintain an adequate sample size without losing granularity, we divided the dataset into five periods of five years each: i)  1979-1995, ii) 1996-2000, iii) 2001-2005, iv) 2006-2010, and v) 2011-2015.
We adopted a clock-time logic to maintain an adequate sample size. As a robustness check, however, we also conducted TM for the entire corpus  by year without accounting for the fields, and the results for these were consistent with those presented in the following sections of the paper. The first paper explicitly focused on consumer identity was published in 1979, while 1995 served as a turning point in CIR. For instance,  Firat and Venkatesh (1995)  won the best paper award in 1998, for their theoretical work on identity construction in the postmodern era. Furthermore, the work of  Schouten and McAlexander (1995)  on subcultures of consumption is highly influential for CIR  (Wang et al. , 2019). Thus, 1979 represents the beginning of our dataset of academic outputs with 1995 being an informal turning point in CIR.
Methods We followed standard procedures of recovering and cleaning data and ran TM to visualize the results  (Muñoz-Leiva et al. , 2021). To facilitate text mining, the corpus of academic outputs was pre-processed by removing numeric characters, punctuation symbols, and general stopwords (e. g. , "and", "so"), to facilitate interpretation  (Humphreys & Wang, 2018). In this stage, we also created and used our own context-specific stopwords dictionary (in Appendix), to ensure that relevant polymorphous words/concepts were not inadvertently removed. When necessary, words in plural were changed into singular, and concepts with similar meanings were also changed (i. e. , communities to community, brands to brand; but we kept branding). We used TM to identify latent topics in each of the five periods, along with the probabilities of their occurrence.
Distinct Existing Groups The first stage of community emergence represents a state before the actual emergence, as identified within the first period (see Table  3 ). During the first period, no mindset was present in most of the academic outputs in our dataset. The probability of each mindset appearing in an academic output averaged between 13 and 20%. In essence, this translates to the community not exhibiting a similar mindset as its members did not use the same conceptual resources. No mindset accounts for more than 20% of the academic outputs, and each group appears to focus on field-related topics. In Fig. 1a , we visually depict this state, which showcases only a few connections among fields. However, the first period sheds light on the origins of the community. As indicated by the analysis of the entire corpus of academic outputs, "social" and "culture" are key resources of the community.
RMT emphasizes the importance of resources in the development as well as the success of groups, and portrays that groups tend to develop through sufficient resource mobilization by their members. We further contribute to the theory by showing that resource mobilization can give rise to a shared mindset, while multiple resources need to be incorporated into the practices of an emergent community. Our work has a clear focus on the extant IS research agenda  (Struijk et al. , 2022)  and its theoretical implications can go beyond the focal topic of community emergence. In doing so, we ultimately help IS theory to take steps away from the traditional "what" questions and move towards the more insightful "how" ones, while we contribute to the line of IS research on theorising using large datasets and novel computational approaches  (Kar & Dwivedi, 2020).
There were no particular conditions for participating in the survey. The survey follows a similar protocol for each participant. The participant begins by reading a consent form on data collection. The eye-tracker is calibrated and in parallel the different tasks and instructions are explained (5 to 10 minutes total for this preliminary part). The participant begins the survey and the instructions are recalled for each task and textual indications are given for tasks 3 and 4. At the end of the experiment, the data is recorded and anonymised. The participants used a web application that we developed for the survey, using Open Layers when OSM maps are rendered, and the Google Maps API, when Google Maps are rendered.
6e -05), which confirms that some tasks are longer to solve than others. The analysis of the influence of the map backgrounds is done with the data from stage 2 on task 1. In particular, we are focusing on the visibility of the railway line at the scale of the city. The majority of participants used the railway line as a landmark. However, the railway is less visible on the city scale on Google Maps than on OSM. It is thus possible to see whether a person managed to locate the railway easily if he zoomed directly on a railway or not. The fixation points above zoom level 16, located near the different railways were selected and analyzed (Figure  10 ). The number of fixations in the areas near the railway tracks do not follow a normal distribution, so a Kruskal-Wallis test is run to analyze the effect of the map on the fixations, wich appears to be moderate (p = 0.
118). To conclude, this experiment shows that eye-tracking is an interesting method to study the behaviour of users of panscalar maps. The analysis of the gaze behaviour confirms the use of landmarks in locating tasks  (Touya et al. , 2023) , and we identified different types of gaze behaviour during a zoom or a pan action. However, we think that the experiment dataset can be analysed further, in particular the gaze behaviour during the tasks with textual instructions. But we also would like to use more the vector data, as we did for the railway line (Figure10), to more closely analyse the gaze behaviour. Furthermore, we think that the experiment protocol has limitations that need to be addressed in further studies. In particular, we do not know why participants are looking at a specific location of the map, and the eye-tracker used is sometimes not precise enough to distinguish between several landmarks.
Data Capture The experimental protocol, as described earlier, consisted of asking each participant to walk through the same path twice: the first time, using navigation assistance (an application installed on a smartphone or AR glasses) and the second time, without using any device. Several datasets were collected during the study to address the defined research questions. Each participant was asked to answer the SUS questionnaire, before and after using the technology for the walk. This subjective assessment of the technology could help to explore the correlations with the effects of spatial knowledge acquisition. The Santa Barbara Sense of Direction Scale (SBSOD) survey offers an indication of the inherent sense of direction of each participant. The SBSOD questionnaire was used to evaluate the spatial abilities of the participants.
Based on these results, participants were allocated to the two groups to ensure a balanced representation of abilities. The findings, using a Welch's t test, showed no significant difference between the two groups in terms of the spatial navigation and sense of orientation abilities. A key dataset in this experiment involved a number of memory tests that participants were asked to perform after the first walk and a week later, both before and after they performed the walk without any technology. The aim of the memory test was to capture the participant's recall of the landmarks and to test their abilities in spatial transformation as an indication of spatial knowledge acquisition  [15]. Specifically, participants were asked to plot their walk on a map, as a way of mentally changing their perspective of the environment (transformation), and to indicate the location and type of landmarks along the path (see Figure  10 ).
The work by Afrooz et al. focused on the comparison between active navigation and passive navigation to assess their effects on spatial and visual memory during wayfinding  [44]. In this work, the authors asked participants to navigate through a predefined route within a university campus, either by following the experimenter (passive navigation) or by leading them (active navigation). The authors did not employ a virtual environment to investigate wayfinding (as commonly performed in other similar works) in order to ensure the direct response of any findings to the real physical environment  [45]. A university campus rather than a city was selected to control confounding variables and avoid factors affecting the data collection process, such as crowds and noise. Knowledge acquisition was assessed through a range of tests involving scene recognition, recollection of the leftright orientation of scenes (mirror image discrimination), and route recollection (sketch maps).
Acknowledgments: The authors thank the  Département du Nord  and the  Agence Nationale pour la Rénovation Urbaine (ANRU)  for their financial support within the framework of the  ValMobile  project as well as the PRIMOH research pole. They thank the  I-SITE Univ. Lille-Europe  (mobility grants:  EWAID  & Go-Smart). They also thank all the participants of the study. They are also grateful to the reviewers for their many constructive comments.
The fairness of decisions made at various stages of the publication process is an important topic in meta-research. Here, based on an analysis of data on the gender of authors, editors and reviewers for 23,876 initial submissions and 7,192 full submissions to the journal eLife, we report on five stages of the publication process. We find that the board of reviewing editors (BRE) is men-dominant (69%) and that authors disproportionately suggest male editors when making an initial submission. We do not find evidence for gender bias when Senior Editors consult Reviewing Editors about initial submissions, but women Reviewing Editors are less engaged in discussions about these submissions than expected by their proportion. We find evidence of gender homophily when Senior Editors assign full submissions to Reviewing Editors (i.
eLife's review process broadly involves two main stages: initial evaluation of submissions by the eLife editorial team, and evaluation of full submissions together with external reviewers (see Fig  1A ). While the initial evaluation of submissions involves an internal consultation among eLife editors, the ensuing step of handling the review of full submissions includes community-facing interactions with external experts. eLife has been collecting meta data on all editorial interactions along this two-stage process, allowing to analyse not only women editors' representation in the editorial board, but also their active participation along the different stages, thus teasing apart potential versus actual engagement of women. For these reasons, eLife provides a rich case example to evaluate gender imbalance along key decision-making processes in STEMM and in particular in STEMM journals' editorial process.
The aim of this study was to determine whether the involvement of individuals in eLife's BRE is subject to gender disparities at various stages of the editorial process. Specifically, we sought to determine whether women eLife editors are proportionally involved in the editorial decision process compared to their representation in the BRE. To address this question, we explored fully anonymous analytics collected by eLife's editorial platform. This data was collected for monitoring purposes with the explicit aim to help improve eLife's submission and review process. The analytics provided binary gender information ("man" or "woman" as assigned by the editorial office based on scientists' names and perceived gender expression) relating to the handling of submissions. We assessed the presence of gender imbalance at different stages of editors' participation, starting with the external influence of authors who are invited to nominate potential editors (and appeal their decisions), through to the engagement of Reviewing Editors (REs) by Senior Editors, and then ending with the responsiveness of REs to editorial assignments.
Discussion Gender imbalance in the scientific publishing process is already evident when considering simple numerical disparities, starting with women's representation in scientific editorial boards  [67] [68] [69] [70] [71] [72] [73] , number of invited articles  [18, 19, 21] , frequency of being asked to referee  [75] [76] [77] , published manuscripts' topics  [34] , and number of publications  [36, 37, 78]. Here we extend the scope of this disparity by reporting clear under-representation of women in the BRE of a prominent biomedical journal (eLife). Beyond numerical proportions, the eLife dataset allowed us to examine whether the various actions that make up the editorial process are related to RE gender. We find that gender disparity stretches well beyond the known numerical imbalance, hinting at gender biases influencing the editorial process.
The eLife dataset Before we discuss our key findings, it is important to consider our unique dataset and the potential advantages and limitations inherent to it. As detailed in the Methods section, we used anonymous analytics collected by eLife's editorial platform for monitoring purposes. This rich dataset reflects a real-life process, and spans a relatively large range of biological disciplines and international contributing scientists and editors. During the investigation period, eLife had a similar fraction of women in their BRE relative to other editorial boards  [73] , suggesting that the issues identified here are likely to be observed in other journals. However, the specific factors that we could study were not pre-determined based on our experimental needs. Accordingly, we were limited in our explanatory power, both in terms of other relevant factors that might be contributing to the observed effects (e.
g. , the level of seniority of each RE) and in terms of the statistical power (e. g. , authors' appeals are rather infrequent). To mitigate some of these gaps, we can gain some insight from more recent data relating to the heterogeneity of eLife's BRE (see Supplemental Section, S1 Fig), although these recent analytics may not fully represent the dataset we analysed here. It is also important to consider the makeup of the BRE; these are invited roles, and as such, all the REs are established in their subfields. However, due to issues we expand upon below, it is possible that women REs are less senior than men REs, as described in the Supplemental data. Unfortunately, the fact that our primary datasets lack direct information on academic attainment levels for all women and men is a limitation.
This lack of information should not be interpreted to mean that academic status is equal across genders in our datasets, an assumption that is likely to be incorrect. The sociology of gendered behavior predicts that both academic status and gender likely influenced the outcome of the interaction in which manuscripts were evaluated, as discussed below. We also do not have data on the intersectionality of gender with other primary sources of disparity (e. g. , geographic location, race, ethnicity, class, sexual orientation, and ability  [79] [80] [81] [82] ). Yet, the results of a recent eLife self-report survey conducted outside our study period suggest that women serving as editors are more likely to also self-identify as belonging to an underrepresented or minority group based on their race or ethnicity  [83].
It has been previously shown that it is more difficult to voice a different opinion once an opinion has been formed  [89, 90]. The delayed response, as well as reduced response rate (by approximately 3%) could potentially be attributed to the fact that women have more duties and responsibilities than men REs. There are multiple reasons to suggest this, depending on women's specific intersecting identities  [43, 91, 92]. For example, senior women are overburdened by administrative responsibilities due to the institutional need to narrow the gender gap  [25, 30]. More specifically to our dataset, there is a hint that women REs are at an earlier career stage relative to men (Supplementary section), and hence may be more likely to have children at home than their men colleagues and thus face an added burden on their time  [43] , or be more laden with obtaining tenure.
Similarly, gender homophily in reviewer assignment by journal editors was widespread among men editors, while for women only a small number of highly homophilic editors dominated  [67]. Our data did not allow us to directly explore the prevalence of homophily among individual REs, yet the fact that homophily was widespread across many fields, involving different REs, suggests women homophily is a broad phenomenon in eLife. Additionally, we find that homophily increases with gender balance across sub-disciplines. This echoes the finding that men homophily in article citations increases as the research field gets more gender balanced with time  [54]. However, given that women Senior Editors are outnumbered by men (for example, 36% (30) women vs. 64%  (52)  men Senior Editors in 2021), on average we see an over-engagement of the men REs, even after accounting for their numerical dominance in the BRE.
Data Data accumulated by eLife's platform for science publishing over the years 2017-2019 were organised into two datasets, as summarised in Table  3. The first dataset will be referred to as the BRE dataset, and the second will be referred to as the Manuscript dataset. We will describe each in turn. But first we make a note on assigned gender. Gender assignment. In all cases, Editor gender was assigned by eLife's staff based on the editor's name and gender expression. Note that staff (i) assigned a binary "man" or "woman" gender, (ii) did not distinguish between trans and cis identities, and (iii) did not assign other genders such as nonbinary, genderqueer, agender, or genderfluid. Note that any editor could Table  2. Summary of the study's main findings, speculated causes, and potential solutions.
Accordingly, the assignment of gender therefore reflects not self-identity but rather the perceived binary gender of the person. This perception is likely to also be held by the majority of the broader community, and hence is particularly relevant to understanding how the editor might be treated by that community (e. g. , the frequency with which they might be suggested as a Reviewing Editor by authors). We also note that since early 2020, eLife has given all Senior and Reviewing Editors the option of sharing their self-reported gender identity via a confidential survey. However, the current response rate (~40%) precludes a comprehensive analysis of gender disparities using the data at this stage  [83]. BRE dataset. This dataset includes anonymous information relating to the engagement of individual REs in the editorial process.
This information includes the start and end dates of their editorial contracts, the number of consultations in which they have been invited to participate, how responsive they are to consultation requests (number of responses and response rate), the number of full submissions assigned, and how many days they take to make an editorial decision. In addition, the editorial staff asks REs to provide a set of keywords that reflect the scope of their research, which was also included in this dataset. Note that the terminology was self-generated by the REs (rather than adopted from an existing database), and that there were no limitations on the number of keywords each RE could provide. For some REs, additional keywords are added by the editorial staff based on the information publicly available on the editors' academic websites.
Effect Manuscript dataset. This dataset includes information relating to each manuscript submission, detailing the manuscript's outcome in each of the submission stages. This dataset also contains the assigned gender (as described above) of those BRE members that were suggested by the authors, the recorded gender of the handling RE, and the recorded gender of the assigned Senior Editor. Note that here our information regarding gender pertains only to the editorial team handling the manuscripts and not to the manuscript authors, whose identities were not made available for the present study due to ethical considerations (though we note that the authors' identity, but not necessarily their self-defined gender, was known to the editors involved in the assessment). Manuscripts with appeals received after the Initial Submission and without a Full Submission decision were most likely rejected prior to review.
It is possible that a small fraction of manuscripts was withdrawn prior to evaluation; however, we did not have access to such data. Additionally, this dataset contains up to two (out of 18) disciplines that the authors assigned to their manuscript upon submission. Options included 'Neuroscience', 'Cell Biology', 'Developmental Biology', 'Structural Biology and Molecular Biophysics', 'Microbiology and Infectious Disease', 'Biochemistry and Chemical Biology', 'Chromosomes and Gene Expression', 'Genetics and Genomics', 'Computational and Systems Biology', 'Immunology and Inflammation', 'Cancer Biology', 'Medicine', 'Evolutionary Biology', 'Physics of Living Systems', 'Plant Biology', 'Ecology', 'Epidemiology and Global Health', and 'Stem Cells and Regenerative Medicine' (Manuscript Dataset; see Tables  1  and 2 ).
Data analysis We applied several exclusion criteria to the data before proceeding with further analysis. In the BRE dataset, we excluded REs who became Senior Editors, or resigned as Senior Editors (and became REs) in a given year, or those who were inactive (i. e. , were never contacted on initial submissions). In addition, in the manuscript dataset, we limited the number of author-suggested REs to five per manuscript; and excluded papers handled by guest editors as well as Research Advances, Registered Reports, and formats that go through a different workflow.