dataesr/scientific-paragraphs-categorization huggingface dataset:
Approach to evidence We present this preamble as a way of emphasising the analytical perspective we want to take in this paper. As should be already clear, we are less interested in technology 'in itself', in how something like Skype (or its proxies, Facetime, Google Hangouts and the rest, often rather charmlessly described through an acronym, VMC, Video Mediated Communication) works or in how such technologies might be described by, say, marketing and technology vendors; we are much more concerned with the reasoning and other practices that are constitutive of, and thus productive of, everyday life. More especially we are interested in the ways in which language use is part of the life in which technology is brought into play. In our view, it is in the organisation of language that technology gets to be made a socially relevant and shaped phenomenon.
In these respects, our concern echoes, in varying degrees and ways, ethnomethodology  (Garfinkel, 1967)  conversation(al) analysis and membership categorization analysis  (Maynard, 1988; Fitzgerald and Housley, 2015). These approaches variously focus on how ordinary society is, in various ways, incarnate in talk. The move to a concern for language use has important implications for the kinds of evidence that can be brought to bear on investigation as well as in regard to the analytic statements and insights that can be generated from it. All this is worth bearing in mind if one wants to take this approach in one's own research, as we do in this paper. To begin with, this approach opens up where data can be found. For, in this view, it can be found in any type of talk about Skype as well as in the talk one can observe when people are 'Skyping'; when talk is in vivo if you like.
Reasons that we seek are the vernacular of making sense, and this includes how people make moral sense of themselves and others, of their actions and their shared doings. Reasons that we seek are the vernacular of making sense, and this includes how people make moral sense of themselves and others, of their actions and their shared doings. Though we might only want to explore 'reasons to Skype' we think our inquires will point towards how people understand the lives they lead in terms that makes sense to them, that are applicable to what they are about as they understand it and for which they use their own language to articulate. It is to explore the empirical and conceptual implications of this position that we now turn.
Evidence in everyday reasoning about Skype Our explorations are based on transcript data from two separate sets of interviews about Skype with families groups and friends. The first set were conducted in 2014 and were concerned specifically with traditional Skype video callingwith Skyping as a verb if you like. They were all concerned with how Skyping was described and accounted for in the interview talk, and sought to uncover the everyday reasons used to account for and reflexively organise doings with Skype. The second were conducted in 2014/2015 as part of 'at-launch' study of a new form of video connection that piggy-backed on some of the technical infrastructure of Skype. Here, too, the concern was with everyday language and its categorisations, and Skyping was certainly a very significant part of this linguistic landscape.
But here the concern was with how language terms are used prospectively to organise what doings a new application might encourage or entail (see details in  Rintel et al 2016). Crudely speaking, and bringing these two sources of data together, our task was not to investigate how people understood technology (or variants of some basic form) but in the systematic ways language about the world known in common could be seen to be a resource to both understand that world and to fold into it new features as they become engaged with -new features that could be related what the technology enabled or new ways in which people wanted to conduct themselves in their expressed practices. With this evidence, we organise the remainder of the paper as follows. The next section presents the first transcript evidence, and relates the kind of category work that is undertaken when the term Skype is used.
And moving on is what nearly all the interviewees did -in collaboration with us of course. Both they and we wanted to get to other matters -nuances in the doings of Skyping that would merit the interview, whatever they might be. In the bigger transcripts part of the motive for moving on in becomes clearer -in a very crude sense. For the bulk of the interviews were about these nuances, to do with reasons not to Skype given reasons to Skype. Before we can turn to those, we need to note that part of the way these 'reasons to talk about' have the shape they do is that they too are foreshadowed in the initial responses to the question, "Why Skype?" Take the first segment, above; here the interviewee says that, with Skype, their interlocutor ('Mum'), can demand to see something, and demand that can be satisfied given that Skype is being used.
Conclusions Our purpose in this paper has been to examine some of the key features of 'adeptness at talk about Skype'. Our task has not been to show whether people can say wrong or right things about Skype but rather to examine how people invoke Skype as a categorizing device in their utterances that results in that category, 'Skype', coming to be a relevant organizing matter in their affairs. Or, to put this another way, we have examined how invoking Skype or Skyping is a sense making device that can be relevantly brought up in spoken efforts to accomplish a world known in common with fellow interlocutors -friends, family, even interviewers. Skyping brings people together in more ways that just the visual, its use as a term in language makes the world one in common, fabricated through that wordalong with many others of course.
Our paper shows how Skyping is to be seen through the prism of language and language praxis, and it is through language that one can see how Skype comes to matter. We might draw on analogy here with the subjects of an article by Garfinkel et al on the activities of professional astrophysicists  (1981: 131-58). These astrophysicists have reached the pinnacle of their educational and professional careers, but it is not that status that matters to Garfinkel and his colleagues. The work that Garfinkel et al are interested in is how these professionals need to do linguistic category work. This work entails fitting empirical evidence to appropriate categorisations and descriptions; evidence to words and through words back to evidence that does the trick it needs topersuades all involved in that setting that what they think they are talking about is indeed real, a thing worth talking about.
Linguistic data. We focus on the languages that immediately surround Bangime, and those that have potentially had a historical impact on the language through past contact with its speakers. This resulted in a total of 38 languages from 4 languages families, as shown in Table  1. The sources for the data are as follows: Dogon data come from the Dogon Languages Project lexical database  (Heath et al. , 2015). The subgroupings for the Dogon languages are based on an on-going phylogenetic study of the Dogon languages  (Hantgan, 2019). Bangime data are from  Heath et al. (2019). Those from Songhai Kiini and Senni are drawn from  Heath (2005b)  and  Heath (2015)  respectively. Jenaama data are from Heath  (2016). Remaining language data are from the pan-African lexical database RefLex (http://reflex.
cnrs. fr, Segerer & Flavier, 2011 -2022) , (with specific source information given in the supplemental materials), and the first author's knowledge of Fulfulde and Bambara. Mande and Atlantic classifications are based on Glottolog  (Hammarström et al. , 2021). There are 38 languages represented in the sample of Malian languages used for this study. The number of concepts selected was 348. As some languages have more than one form, and others are missing certain concepts, the total number of words in the lexical dataset is 9577. From a statistical standpoint, a limitation of this dataset is its somewhat skewed coverage. Despite this, we decided not to remove languages or concepts as was done in Hantgan and List (personal communication), because of the qualitative aspect of the study; it was crucial for us to examine individual lexical items in the borrowing context despite the fact that they were not represented across all languages so as to at least find tendencies and directions for future, broader, studies.
5). This procedure can be done quickly, because the Concepticon project now offers a variety of tools, including an automated mapping procedure for full concept lists, which can then be quickly manually refined, and a convenient lookup-tool for individual elicitation glosses in different languages (https://digling. org/calc/ concepticon, see  List et al. , 2018). In order to harmonize phonetic transcriptions provided in the different sources, we make use of orthography profiles  (Moran & Cysouw, 2018) , which allow for a convenient conversion of graphemes (potentially consisting of more than one symbol) in one transcription system to graphemes in another. Orthography profiles are provided in a very straightforward tabular structure consisting of two basic columns, one representing graphemes in the source transcription and one representing the corresponding grapheme in the target transcription.
Orthography profiles do not only allow us to convert text in one transcription system to text in another, they also allow us to simultaneously segment the transcription into meaningful units. For the purpose of lexical comparison in contact and historical linguistics, these units are distinct sounds in a given language. To guarantee that these sounds constitute meaningful units beyond our given data sample, we made sure that all distinct sounds in our sample can be linked to the Cross-Linguistic Transcription Systems reference catalog (CLTS, https://clts. clld. org, List et al. , 2021a, Version 2. 1), which offers references to more than 8000 speech sounds and allows for a convenient translation between different transcription systems (see  Anderson et al. , 2018  for an overview on CLTS).
The data lifting was carried out with the help of the CLDF-Bench package (https://github. com/cldf/cldfbench, Forkel & List, 2020), a software suite that allows us to combine the different stages of data lifting in a reproducible way, using the workflow that was established for the Lexibank repository of cross-linguistic wordlists (see List et al. , 2021c, https://github. com/lexibank/lexibank-analysed). To make sure that the data are comparable beyond the scope of a single application, CLDF-Bench essentially converts the data to the CLDF format, recommended by the Cross-Linguistic Data Formats initiative  (Forkel et al. , 2018). For the specific analysis we employed in this study, we further converted our data to the tabular format required by the LingPy software package (https://lingpy. org, List & Forkel, 2021, Version 2.
Linguistic analysis A Automated identification of borrowing candidates Methods for the automated identification of cognates have largely increased in accuracy over the past decade (see List, 2014 for an introduction to basic techniques and List et al. , 2017 for an example on the comparison of different methods). While these techniques were primarily designed for the detection of genetically related words, scholars have repeatedly shown that they also can be used to identify borrowings (compare  Van der Ark et al. , 2007, and Mennecier et al. , 2016, and    for an overview). In order to identify borrowing candidates automatically, it is advisable to use those cognate detection methods which are based on the identification of phonetic similarities among the languages in question, as opposed to those methods that try to identify deeper similarities based on automatically determined sound correspondences.
For the purpose of identifying an initial set of borrowing candidates in our dataset of languages of central-eastern Mali, we use the Sound-Class-Based Phonetic Alignment algorithm (SCA, List, 2012), as it is provided as a method for automated cognate detection by the LingPy software package (List & Forkel, 2021, Version 2. 6. 9). The SCA method starts by assembling pairwise distance scores derived from sound-class-based alignments for each word pair in a given concept slot across all languages in the sample and then uses a flat clustering algorithm to partition the words into sets of cognate candidates. In order to distinguish potential language-family-internal cognates from borrowings across families, we apply a filter that retains only those cognate candidates which occur in at least two different language families.
B Manual refinement of automatically identified borrowing candidates Given that automated methods in historical linguistics still cannot compete with trained experts, we checked and refined the automatically identified sets of borrowing candidates manually. For this step of the analysis, we employed the Etymological Dictionary Editor (EDICTOR, https://digling. org/edictor/, List, 2021, Version 2. 0), a web-based tool for the creation, curation, and annotation of etymological data in historical linguistics. The EDICTOR tool was written in JavaScript and offers an efficient framework for the annotation of cognate sets (see List, 2017 for an overview). Since our Bangime-based data do not consist of proper cognate sets, we slightly modified the traditional conventions for the annotation of borrowing candidates by providing zero identifiers for all those words that could not be identified as being shared across different language families, while using the typical numeric identifiers for cognate sets to assign words to the same sets of borrowing candidates.
As a result, the investigation of sound correspondence patterns often cannot be applied to the search for layers of contact. But when searching for potential hints on earlier contact scenarios, sound correspondences are not the only kind of evidence available to the linguist. First, semantic criteria may be used to check to which degree identified borrowing candidates belong to a coherent semantic field; we can take these "distributional properties of shared traits" (List, 2019: 4) as evidence for a contact layer. Additionally, we can inspect the distribution of borrowing candidates across the languages in our sample. Assuming that language contact does not happen constantly, but rather in phases in the development of a language family, we can try to identify distributions that reflect past contact situations.
So far, in historical and contact linguistics, sufficient work has yet to be done with respect to the analysis of distributions of borrowings, and it is not entirely clear how large the dataset needs to be in order to be sufficient for valid quantitative approaches. Our goal is to provide a starting point for future studies on Bangime and its neighbors, be they quantitative, qualitative, or computer-assisted, thus it would be unrealistic to propose a full-fledged method for a loan distribution analysis here. We therefore apply a rather rough method for gaining first insights into the distribution of borrowing candidates in our data by investigating only those borrowing candidates which appear exclusively across two different language families. The basic idea is that-if enough of these examples can be found-borrowing candidates attested between family pairs alone could directly hint to past contact scenarios, while borrowing candidates covering several language families are much more difficult to analyze.
D Implementation The methods described here are implemented in the form of Python scripts and are available along with the data and detailed instructions on the installation of required third-party libraries in the supplementary material accompanying this study. Babiker et al. (2020)  investigated the genetic structure of populations across the Bandiagara Escarpment and the sandy plains of central-eastern Mali by genotyping ~600,000 SNPs for 210 individuals from 10 populations representing five ethnic groups (Bangande, Bozo, Dogon, Fula, and Songhai). The authors analyzed genome-wide data together with data from regional sets of populations and provided the detailed genetic structure of the populations from this region of West Africa  (Babiker et al. , 2020).
M Dogon-Mande (111) In principle, at least, Dogon and Mande are related at the higher order of the Niger-Congo language phylum. This is reflected, whether by contact or a common inheritance, by extensive shared vocabulary in our dataset. Phonotactically, the difference is difficult to distinguish as sound correspondences can be found in both cases. Semantically, certain concepts such as BOAT are likely a recent introduction into plains Dogon languages from Bozo fisherman, whereas the origin of concepts such as MARROW, with variable forms and pairings within the subgroups, is less clear. Synonyms and semantic extensions are a crucial component of any study of borrowed lexical items. Borrowings are common among our wordlist when a concept is expressed through more than one word. An extended meaning of the concept HEAD is an areal feature found throughout all the groups in our sample except Mande.
The closest Bambara speakers are found in cosmopolitan areas such as the regional capital Mopti where Bangande travel occasionally for work or business interests. Otherwise, the country's capital city Bamako is quickly becoming a frequent destination for Bangande seeking salaried jobs. Naturally, migrants return to their home in the Bandiagara Escarpment, but these trajectories mostly affect young people and our data were gathered from consultants who had spent the majority of their lives in the village and environs. Additionally, we can see a clear theme, especially among the direct borrowings from Bambara into Bangime. Granted, Bambara is the only west Mande language in our sample, and thus these forms likely have Mande cognates among languages of that sub-grouping. However, it is worth noting that many concepts are shared between Bangime and Bambara to the exclusion of related Mande languages or surrounding Dogon languages.
Pertinent to our study, he uses comparative Bangime-Dogon-Songhai lexical data to support his claim of a lost Nilo-Saharan substrate which he calls 'Plateau' as he believes there is evidence for traces of this lost language among the cliff-dwellers today. Of the 12 lexemes he provides, over half are monosyllabic, thus increasing the likelihood of chance resemblances. While certain others are somewhat convincing, such as NOSE, Hantgan and List (under review) have discussed the similarities between Bangime body parts and those of Dogon but with mixed meanings; see NOSE in Bangime in comparison with EAR among many of the Dogon languages. Others such as CLOUD and RIVER are considered in our sample to be borrowings, rather than cognates, as Blench suggests for the former (albeit he admits the latter).
Tey have very low borrowing scores and high age scores in the World Loanword Database(Haspelmath & Tadmor, 2009, https://  wold. clld. org). Not only are these concepts numerous, one must note the prevalence of body parts and core vocabulary. Furthermore, honey cultivation from bees is essential to Bangande agricultural practices, yet the form for HONEY is shared. Given the discussion above in Section 2. 5, that both the concepts SONG and SING are shared between Bangime and Tommo So is not surprising. D Bangime-Northwestern DogonMost concepts shared between Bangime and the northwestern Dogon languages are also shared with the southwestern group, or with the Dogon languages more generally. Bondu So is the most prominent language in this group. For instance, Bangime shares lower numerals with Bondu So, which in turn patterns with the Dogon languages more generally.
Acknowledgements We also gratefully acknowledge the support of Director  Russell D. Gray  for hosting  Abbie Hantgan  as a visiting researcher at the  Max Planck Institute for the Science of Human History  in Jena in 2017, as well as the continued support of the Dogon and  Bangime Linguistics  project, particularly PI  Jeffrey Heath  and  Steven Moran  whose work on comparative Dogon fed the initial stages of this project.
Data availability Underlying data Zenodo: underlying data for 'First steps towards the detection of contact layers in Bangime: a multi-disciplinary, computerassisted approach'. https://doi. org/10. 5281/zenodo. 5751226 This project contains the following underlying data: the raw data used for the study before and after it was processed using the computer-assisted methods outlined above, the sources of the original data, the scripts used to process the data, and the borrowing plus cognate patterns found in the data.
An Improved Database of Cross- Linguistic Colexifications Assembling Lexical Data with Help of Cross- Linguistic Data Formats. Linguistic Typology. 2018; 22(2): 277-306. Publisher Full Text List JM, Anderson C, Tresoldi T, et al. : Cross-Linguistic Transcription Systems (Version v2. 1. 0). [Data set]. Zenodo. 2021a. Publisher Full Text List JM, Forkel R: LingPy. A Python library for historical linguistics. Version 2. 6. 9. With contributions by Greenhill, Simon, Tresoldi, Tiago, Christoph Rzymski, Gereon Kaiping, Steven Moran, Peter Bouda, Johannes Dellert, Taraka Rama, Frank Nagel. Leizpig: Max Planck Institute for Evolutionary Anthropology. 2021. Reference Source List JM, Rzymski C, Greenhill S, et al. : CLLD Concepticon 2. 5. 0 [Data set]. Zenodo. 2021b. Publisher Full Text List JM, Forkel R, Greenhill S, et al.
SCA: Phonetic Alignment Based on Sound Classes. In List JM: : New Directions in Logic, Language, and Computation. edited by Marija Slavkovik and Dan Lassiter, Berlin; Heidelberg: Springer. 2021; 32-51. Publisher Full Text List JM: Sequence Comparison in Historical Linguistics. Düsseldorf: Düsseldorf University Press. 2014. Publisher Full Text List JM: A Web-Based Interactive Tool for Creating, Inspecting, Editing, and Publishing Etymological Datasets. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. System Demonstrations, Valencia: Association for Computational Linguistics. 2017; 9-12. Publisher Full Text.
Automated methods for the investigation of language contact, with a focus on lexical borrowing. Linguistics Lang Compass. 2019; 13 (10): e12355. Publisher Full Text List JM: EDICTOR. A web-based interactive tool for creating and editing etymological datasets. Version 2. 0. 0. Max Planck Institute for Evolutionary Anthropology: Leipzig. 2021. Reference Source Manning K, Pelling R, Higham T: 4500-.
Background: Physical multimorbidity (i. e. , ≥2 chronic conditions) may induce feelings of wish to die (WTD), but there is limited literature on this topic, while the mediators in this association are largely unknown. Thus, the aim of the present study was to investigate this association and its mediators among older Irish adults. Methods: Cross-sectional, nationally representative data from Wave 1 of the Irish Longitudinal Study on Ageing 2009-2011 were analyzed. Information on self-reported lifetime diagnosis of 14 chronic physical conditions were obtained. WTD was defined as answering affirmatively to the question "In the last month, have you felt that you would rather be dead?" Multivariable logistic regression and mediation analyses were conducted. Results: Data on 2941 adults aged ≥65 years [mean (SD) age 73.