dataesr/scientific-paragraphs-categorization huggingface dataset:
Random forest parameters were tuned using a random 10% of the data with the tuneRanger package in R (λ, split features = 6 and minimum node size = 3; C, split features = 17 and minimum node size = 3)  (Probst et al. , 2018). 4. Each trained random forest was provided with the observed summary statistics, and each regression tree was used to generate posterior distributions for both parameters. Calculating all nine summary statistics for each of the four years means that we conducted ABC with 36 summary statistics in total. Random forest ABC is robust to the number of summary statistics  (Pudlo et al. , 2016)  so we did not have to reduce the dimensionality prior to inference  (Blum et al. , 2013). As an additional robustness check, we fit the agent-based model to simulated data from two combinations of known parameter values: (1) λ = 3 and C = -1 (intermediate complexity and pressure for distinctiveness); and (2) λ = 3 and C = 1 (intermediate complexity and random copying).
Shuffling model (second aim) The shuffling model takes a set of brands and calculates a predicted prevalence for every possible combination of the components within those brands. When it performs well, the shuffling model assigns a higher predicted prevalence to brands that actually exist in the data. We predict that this will be more true for time-and space-mixed datasets than for the original, structured datasets. According to the shuffling model, the predicted prevalence of a brand is simply the product of the frequency of its components in a specific subset of data. For example, the predicted prevalence (S ) of a brand with three components A, B and C would be: S ABC = F A × F B × F C × N other where F A is the frequency of A among brands that do not include B or C, F B is the frequency of B among brands that do not include A or C, F C is the frequency of C among brands that do not include A or B and N other is the total number of brands excluding the focal brand.
To apply this model, we first needed to generate structured and mixed datasets. The structured datasets were split into groups according to time, space and complexity. For time, brands were grouped into two age groups: the 'old' group (O) included all brands that appear in the 1990 brand book, and the 'young' group (Y) included all brands that appear in later brand books. For space, brands were grouped into four rectangular areas of roughly equal size following county borders: northwest (NW), southwest (SW), northeast (NE) and southeast (SE) (see Figure  S2 ). For complexity, brands were grouped into two categories: two-component and three-component brands. One-component brands were excluded because they lack the combinatorics required for the shuffling model, and fourcomponent brands were excluded because they are relatively rare in the dataset (∼2%).
This structure was used to split the data into 16 subsets, each of which represents a unique combination of time, space and complexity (e. g. O-NE-2 is old northeastern brands with two components). Once we had structured datasets we generated time-and space-mixed datasets to test our hypotheses about the effects of time-and space-averaging. Each mixed dataset has as many brands as the structured dataset that it imitates. For time-mixed datasets, brands were randomly drawn from the same area independently of time. The time-mixed O-NE-2 dataset was constructed by randomly sampling n two-component brands from northeast Kansas across all time periods, where n is the number of brands in the structured subset. For space-mixed datasets, brands were randomly drawn from the same time dataset independently of space.
The space-mixed O-NE-2 dataset was constructed by randomly sampling old two-component brands from all of Kansas. For each structured and mixed dataset, we inventoried all of the components present in the brands, listed all of their possible combinations, and used the shuffling model to assign a predicted prevalence to each combination. For each possible combination we noted whether it was actually present in the dataset or not. To compare the accuracy of the shuffling model when applied to the structured and mixed datasets, we built two linear mixed effects models with predicted prevalence as the outcome variable: one with the structured and time-mixed datasets, and the other with the structured and space-mixed datasets. Whether the dataset was mixed (MIXED: 0 = N, Y = 1), whether the combination of components was actually present in the data (ACTUAL: 0 = N, Y = 1) and complexity (COMPLEXITY: 2 = 0, 3 = 1) were potential fixed effects.
Space, time and the specific combination of components were potential varying intercepts. The shuffling model assigns a predicted prevalence of 0 when a component occurs multiple times in a target brand or only occurs in a single brand in the dataset, so all combinations with a predicted prevalence of 0 were dropped from the modelling. The remaining predicted prevalence values fit a lognormal distribution so we log-transformed them prior to modelling. Model choice was conducted using Akaike's information criterion (AIC) calculated from frequentist models fit with the lme4 package in R  (Bates et al. , 2015). The best fitting versions of each model were run as Bayesian models in Stan using the brms package in R  (Bürkner, 2017) , using mean field approximation with 5000 samples from 50,000 iterations.
Our first prediction was that ACTUAL = 1 would have a positive effect on the predicted prevalence of a combination of components, which would indicate that the shuffling model works better than chance. Our second prediction was that there would be an informative and significantly positive interaction term ACTUAL*MIXED, or in other words that the predicted prevalence differential between ACTUAL = 1 and ACTUAL = 0 is more important when MIXED = 1. This would indicate that the performance of the shuffling model is higher in mixed datasets. Finally, complexity was included as a control variable because three-component brands have an additional multiplied proportion that probably reduces their prevalence values.
Temporal distance (supplemental analysis) The structured datasets used for the shuffling model were also used to test whether brands that are closer to each other in time tend to be more similar. First, we pooled together both the old and young brands from each spatial subset (e. g. O-NE-2 + Y-NE-2 = NE-2) and calculated the Levenshtein distance (LD) between each pair of brands. As a reminder, old brands are those that appear in the 1990 brand book and young brands are those that appear in later brand books but not in the 1990 issue. Then, we ran a linear mixed effects model with LD as the outcome variable, whether the brands are both old (OO), both young (YY) or one of each (OY) as the predictor variable, and the identities of the compared brands as random effects (i. e. one intercept for the first brand and one for the second).
Shuffling model (second aim) Brand appears to be the only grouping variable that explains a high level of variance in the data (time-mixed, ICC brand = 0. 926, ICC space = 0. 021, ICC time = 0. 009; space-mixed, ICC brand = 0. 923, ICC space = 0. 003, ICC time = 0. 002), so we included it as a varying intercept in both models. Adding complexity as a control variable improved model fit (time-mixed, ΔAIC = 5120; space-mixed, ΔAIC = 4852). Adding ACTUAL as a fixed effect improved model fit (time-mixed, ΔAIC = 1377; space-mixed: ΔAIC = 2388) and adding ACTUAL*MIXED as an interaction further improved model fit (time-mixed, ΔAIC = 23; space-time, ΔAIC = 403). The best fitting models for both the time-mixed and space-mixed datasets had the following specification: log (S) ≈ Normal (m i , s) m i = a BRAND[i] + (b C × COMPLEXITY) + (b A × ACTUAL) + (b M × MIXED) + (b AM × ACTUAL × MIXED) a j ≈ Normal (0, 1) b C, A, M, AM ≈ Normal (0, 0.
5) s ≈ Exponential (3) The results of the best fitting models can be seen in Table  2. As predicted, we found that both the effect of ACTUAL and the interaction between ACTUAL and MIXED are positive, indicating that the shuffling model works better than chance and performs better in both time-and space-mixed datasets. Figure  3  demonstrates the accuracy of the shuffling model's predictions when applied to a structured, time-mixed and space-mixed dataset (i. e. Y-SE-2, which has the highest sample size). The effect is subtle, but when applied to mixed datasets (right panel) the shuffling model has more true and fewer false positives, apparently because more of the possible combinations of common components are represented. We found that complexity has the expected negative effect on predicted prevalence.
There are several important differences between cattle brands and typical archaeological datasets that should be highlighted. First, the relative time-scale of averaging in our study is much smaller than that typically seen in archaeological assemblages. The effects of time-averaging generally increase with the aggregation window  (Perreault, 2018; Wilder & Kandler, 2015) , with the onset of effects occurring when the window is about the same as the average lifetime of variants  (Madsen, 2012). Cattle brands are often inherited within families and can have relatively long use-lives, and we suspect that the ∼25-year window of our study is close to the average lifetime of a cattle brand. Our study shows that time-averaging can have a significant impact on the detectability of copying with a much narrower time window than previously assumed.
Acknowledgments. We would like to thank  Lilia Kurnosova ,  Elia Weber  and  Agnes Dietrich  for their assistance with manual coding of cattle brands, and members of our lab, the  Minds and Traditions Research Group at the Max Planck Institute for Geoanthropology , for feedback on earlier versions of this manuscript. Author contributions. M. Y. transcribed the data with optical character recognition and manual coding with research assistants. M. Y. designed and conducted the agent-based modelling with feedback from H. M. and O. M. O. M. and M. Y. designed the time-and space-averaging analysis and M. Y. conducted it. M. Y. , H. M. , and O. M. wrote the preregistration and manuscript. Financial support. This work has received funding from the  Max Planck Society  and from the ' Frontiers in Cognition' EUR  grant,  ANR-17-EURE-0017 EUR.
The first nationally legislated electoral quota was indeed enacted in 1991 in Argentina and many other countries followed suit in the 2000s and well into the 2010s. In 2021, eighty-five countries across all continents had national-level legal provisions to increase women's representation in elected officesof which fifty-seven had implemented quotas and twentyfive reserved seatsaccording to the IDEA Gender Quotas database 1  which provides more details about country-specific legislation including quota type and size. Gender quotas on corporate boards are more recent and less widespread: in 2006, Norway became the first country to introduce a substantial quota requiring that there be at least 40% of women on the boards of public and state-owned companies. Over the past few years, several countries, such as Belgium, France, India, Italy, or Pakistan, have implemented similar legislation.
This scoping review aims at bridging these so far largely separate bodies of literature in view of a broader evaluation of gender quotas as a policy tool: why are quotas adopted in different spheres? How are they implemented, and with what effects on gender inequalities? We argue that an interdisciplinary approach, taking into account the inputs of a diversity of disciplines (addressing different questions, and using different methods leading to complementary findings) leads to a more comprehensive evaluation of how quotas function and the change they bring about. To this effect, we have compiled and made publicly available online 2  a database of 379 references of peer-reviewed academic articles on gender quotas in all their domains of application, produced by scholars of diverse disciplines.
This database includes 12 variables which, for each article, describe the quota domain and geographical zone under consideration, the research question, methodology and discipline, amongst other characteristics. Based on descriptive statistics and a qualitative analysis of this database, we offer a systematic and interdisciplinary scoping review of gender quotas as a public policy tool 3. To our knowledge, this is the first paper of its sort considering that, thus far, 17 out of the 18 literature reviews published in peer-reviewed journals that we have identified in our bibliographical search focus on a single quota domain 4. This article is structured around four parts. In the first section, we present our methodology, that is the search protocol and the selection criteria that we applied to the construction of our sample and database, along with an explanation of the coding of variables and a brief description of our sample.
I. 1. Bibliographic search protocol The present literature review is based on the analysis of nearly four hundred peer-reviewed journal articles in the French and English languages that were gathered through several bibliographical searches on interdisciplinary scholarly databases. The main body of the literature was collected through searches executed on July 1, 2019. On the Scopus database, utilising the search string [title(quota*) AND title (women OR gender OR sex)], three hundred and sixty-four results were obtained, and two hundred and eighty-five references remained after removing non-anglophone or francophone results, books and book chapters, articles from non-peer reviewed medias, and off-topic references. A second set of searches was performed through the JStor database on the same day using the following search strings: [ti: ("gender quota*")] yielded four results, [ti: ("gender quota*") AND ti: (women OR gender OR sex)] forty-six results, and [ti: ("quota*") AND ti: (women OR gender OR sex)] forty results.
Much of the references hence obtained overlapped with the former, the second search however brought several new references to our attention. A second set of searches was later performed using the Dimensions database with the intention of extracting those most recent references regarding gender quotas. Two searches All the references hence gathered were compiled into an Excel spreadsheet and ten variables were defined. The first three are descriptive variables corresponding to article metadata, and aim at facilitating the navigation in the database and the citation of the references. The "code" variable (1) is composed of the first author's surname, followed by the year of publication, and a small-case letter if the author published more than one paper in the same year. The "complete reference" variable (2) gives the full reference of the article in the American Sociological Association citation style.
This diversity of approaches on an aggregate level however masks persisting segmentation of the literature, in terms of disciplines involved, methods and research questions. This scoping review and the regrouping of references coming from different disciplines in a single database are a first step in order to favour a more systematic dialogue. Indeed, the synthetic format of the scoping review aims to facilitate the acquisition by researchers belonging to a given discipline of the inputs of research from other fields. The complementary database provides easy access to the main references and research results from various disciplines on more specific topics or research questions that may be of interest to researchers. Based on our analysis of existing gaps in the literature, we highly encourage researchers to go beyond their initial research question when taking grounds of the existing literature: for example, impact assessment can be considerably improved, in terms of choice of criteria and the analysis of the mechanisms at play, by an understanding of the genesis of the quota in a given context, where it comes from and the way its objectives were politically framed.
These interviews made it possible to better understand the expectations of the new generation of RNN users. The semi-structured interviews took place in a location chosen by the participants, and in compliance with the COVID-19 health measures that were in force in France in June and July 2021. Two interviews took place by videoconference for logistical reasons. The interviews lasted an average of 60 to 90 min and were recorded; the notes taken during the interviews were completed with the recordings. The files were then uploaded into NVivo 12 (QSR International, 2019) and coded into parent nodes and child nodes (Table  2 ) that represented the different themes raised during these interviews. Nodes are used in qualitative research to collect references from interview files about a specific theme to assist in data analysis.
Acknowledgements This work was supported by the  League for the Protection of Birds (LPO) , the  Regional Directorate for the Environment, Planning, and Housing (DREAL) of Brittany and the Economic and Social Research Council (ESRC) of the United Kingdom. The authors would like to thank, in no particular order,  Karine Pothin  ( Réunion Marine Nature Reserve ),  Frédéric Cadène  ( Cerbère-Banyuls Marine Nature Reserve ),  Alain Ponsero  (the  Bay of Saint-Brieuc National Nature Reserve ) as well as all participants for their contributions to this study. All authors read, and approved the submitted version.
Acknowledgements We thank members of the  DePICT scienti c committee: François Beck (OFDT) ,  Renaud Crespin  (CNRS),  Karine Gallopel-Morvan  (EHESP),  Gwenn Menvielle  (INSERM),  Brigitte Metadieu  ( Association Charonne ),  Viet Nguyen-Than (ANSP) , and  Patrick Peretti-Watel  ( INSERM ) for their help in discussing the study protocol, questionnaire, and planned analyses. We also want to acknowledge  Carla Estaquio  and  Antoine Deutsch  from the  French National Cancer Institute (INCa). We would also like to acknowledge the MV2 polling institute in charge of data collection, as well as  CDA  for monitoring and auditing data quality.
Research in context Evidence before this study In December 2020, we searched the Science Direct and PubMed databases for articles on sexual violence experienced by female asylum seekers (AS) in their host countries using the terms "sexual violence," "rape," "sex offenses," "violence," "migration," "female asylum seekers," and "refugees". These databases contain since 2012 results concerning the past year incidence of sexual violence among female asylum seekers (AS), but the sample sizes are either small or limited to asylum seekers included in asylum reception facilities. Additionally, the designs do not describe the total number of female AS living in the study area during the study period, so the proportions presented are based on an undescribed target population size. Qualitative studies conducted to investigate similar populations in Europe have suggested that the months following arrival in the host country represent a period of exposure to SV.
These professional interpreters were experienced in interviewing AS and worked with most medico-social establishments catering to AS in the southern region of France. All females who were contacted were offered health care according to the WHO and UNHCR recommendations for dealing with SV among this population from experienced primary care services close to where they lived and participants who wished to were offered protective measures in collaboration with OFII. This study was approved by one of the French Research Ethics committees under the authority of the Ministry of Research on 18 June, 2021 (approval number 21. 04. 02. 59049) and registered at ClinicalTrials. gov (NCT05007431). All methods were performed in accordance with the Declaration of Helsinki in terms of ethical aspects, information, and the consent of subjects with regard to participation and data publication.
Data collection A questionnaire was administered face-to-face or by phone, when appropriate. The professional interviewers were experienced and trained in research on SV. This questionnaire covered the following sociodemographic data: age, dependent children, couple relationship with a partner in France at the time of the survey, geographical origin (West Africa, Maghreb, rest of Africa, Europe, South America, Middle East, or Asia), level of education (never attended school, below, equivalent to or above high school), income source (none, legal or undeclared work, state aid, or other aid) and accommodation during most of the past year. Females who did not have a single dwelling with a legal lease were classified and we used the European Typology of Homelessness and Housing Exclusion (ETHOS)  21  : Emergency housing support (ETHOS 2 or 3), National Asylum Support System 22 (ETHOS 5) and lack of support system (ETHOS 1 or 8).
Statistical analysis Statistical analysis was conducted using IBM SPSS Statistics version 20 (IBM SPSS Inc. , Chicago, IL, USA). Continuous variables were expressed in terms of means with standard deviation, and categorical variables were reported in terms of counts and percentages. Both types of these variables were expressed with 95% confidence intervals (CI); thus, the corresponding figures for all AS living in France could be estimated from the results. Incidence proportions were expressed with 95% CI. Weighted incidence proportions were calculated to consider age and geographical deviation from the overall source population (OFII file). Comparisons of mean values between these two groups were performed using Student's t test or the Mann-Whitney U test. Comparisons of percentages were performed using the chisquare test or Fisher's exact test, as appropriate for the situation at hand.
Results Between 1 October 2021 and 31 March 2022, 1299 females from the OFII file were identified as eligible for participation n in this study. A total of 897 (69. 1%) of these females were unreachable, 118 (9. 1%) declined to participate, and 11 (0. 8%) missed the inclusion appointment. Two hundred seventy-three (21%) females were ultimately included in the study (Fig. 1 ). Eighty-six interviews (31. 5%) were conducted by phone. Sociodemographic characteristics and sexual violence history prior to arrival in France are reported in Table  1  In our sample, 75. 7% (202/267) of females had experienced SV prior to arrival in France, 139 of whom had been raped (68. 5% of a sample of 203 females after excluding missing data for rape). Regarding female genital mutilation, 40. 2% (80/199) had suffered from this violence; all of whom were from Africa.
6 [95% CI, 1. 9-144. 7] p = 0. 01). With regard to sexual blackmail, females from West Africa (OR = 2. 1, [95% CI, 1. 1-4. 2; p = 0. 03]) seemed to be more at risk. Regarding documented episodes of rape prior to arriving in France, geographical origin in West Africa was found to be associated with rape (OR = 4. a N: number of data available after excluding missing data. 95% CI estimate from the results of our sample the corresponding figures for all female asylum seekers living in France. b  The unweighted incidence proportions are the raw results from our database. The weighted incidence proportions correspond to an adjustment for our age and geographical deviation from the overall source population (OFII file).
Acknowledgements The authors would like to give special thanks to the OFII agents,  Me Grandmottet  and her team, who made it possible to identify all the females eligible for this study. Additionally, we thank the OFII management staff, particularly  Me Le-Luong , for authorizing this study and  Drs. Crocq  and  Sebille  for their comments on the manuscript. The authors thank  Mr Nardini  for his help in constructing Fig. 2 and  Mrs Lepine  for supporting our investigators. Finally, the authors are very grateful to the reviewers for all their comments which improved the quality of the manuscript.