dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion and Future Work In this paper, we have proposed a data model and an implementation for generating text descriptions of intersections. We proposed a way to describe an intersection elaborated with professionals, then presented the model and the implementation used to generate it from OpenStreetMap. After presenting the specificities of this model, we conducted an evaluation phase that allowed us to highlight the model capacity to generate coherent descriptions, with regard to the available data. Our results may be improved in several ways. First, we saw that our implementation may consider more tags to accurately represent the different lanes of a way as it was the cause of some incorrectly generated descriptions. Moreover, the current model and implementation do not consider cycleways that may be present either on the road or on the sidewalk, thus being an important information to describe.
Procedures We explained the aim of Study 1 to the interviewees, stressed the voluntary and anonymous nature of their participation and had all participants sign a consent form. Four interviews were conducted faceto-face/over videoconference and eight by telephone, depending on the preference and geographical location of the interviewees. Although telephone interviews might be less rich in visual cues, they have the advantage of making people feel relaxed, making it easier to disclose sensitive information  (Block & Erskine 2012; Novick, 2008). Further, the interviewers were mindful to ensure that their understanding of what had been said aligned with the interviewee's intention. In addition, because we were personally concerned with the topic of disability and shared our personal accounts, we built trust and created a safe space in which interviewees could develop their narratives, providing rich data  (Irvine et al.
Data analysis For Study 1, all interviews were transcribed in the original language, with subsequent standard back-and-forth translation by two individuals native in both languages  (Brislin, 1986). The researchers worked individually and collectively in a lengthy process of coding for each of the athletes' narratives and across the whole corpus, comparing interviews and discussing emerging themes and links with theoretical perspectives  (Alvesson & Kärreman, 2011). This process included listening to the audio recordings, reading transcriptions multiple times, reflecting, and creating and revising codes and tables of themes that represented meaningful and emotional experiences to the athletes  (Corbin & Strauss, 1990; Spiggle, 1994). This process was undertaken with the help of NVivo software.
The dates are calibrated and converted into calendar ages (cal BP) using the most recent terrestrial calibration curves for the Northern Hemisphere  [87]  and the Southern Hemisphere  [88] , as shown in the electronic supplementary material, table  S1. The comparison between the two datasets reveals that the differences are small given the uncertainties with regards to the effect of the variations in the Intertropical Convergence Zone and its impact on the radiocarbon dates from regions affected by it. For the calibration of marine shell dates, we used the Marine20 curve  [89]  and the local marine reservoir correction (DR) was calculated from the average of the five nearest locations to the respective site for which such values exist (for Pamwak this was DR = -96 ± 29 14 C yr). Bayesian frameworks have been shown to greatly improve understanding of stratigraphic chronologies.
δ 13 C and δ 18 O values were compared against International Standards (IAEA-603 (δ 13 C = 2. 5‰; δ 18 O = -2. 4‰); IAEA-CO-8 (δ 13 C = -5. 8‰; δ 18 O = -22. 7‰); USGS44 (δ 13 C = -42. 2‰)) and an in-house standard (MERCK (δ 13 C = -41. 3‰; δ 18 O = -14. 4‰)). Replicate analysis of MERCK standards suggests that measurement error is ca ± 0. 1‰ for δ 13 C and ± 0. 2‰ for δ 18 O. Measurement precision was studied through the measurement of repeat extracts from a bovid enamel standard (n = 20, ± 0. 2‰ for δ 13 C and ± 0. 3‰ for δ 18 O). Following a Shapiro-Wilk test for normality and assessment of a histogram, a Mann-Whitney U test was used to determine whether there was a significant difference between the δ 13 C and δ 18 O of terrestrial and marine fauna analysed across the sites. Linear regressions were performed on the combined marine and terrestrial faunal datasets for Pamwak, Sasi and Teouma, while a separate linear regression was performed for just the terrestrial fauna at Teouma.
Our study further highlights the lack of early human fossils in the region, with new dating efforts confirming a late chronology for human remains at the sites of Pamwak and particularly Matenkupkum. The application of Zooarchaeology by Mass Spectrometry (ZooMS) holds much promise in this regard (for an overview see  [105] ) and has shown its capacity to identify hominin remains from previously unidentifiable fragmented faunal datasets and has already been shown to be viable in the tropics, including in the Pacific region  [106]. Nevertheless, our isotopic data add to an emerging understanding of how early human arrivals might have adapted to this part of the world. The tropical islands of the Pacific have often been portrayed as challenging prospects for human colonization (e. g. [107,108]), requiring maritime navigation and sophisticated adaptations to depauperate tropical forest settings.
Acknowledgements. The Pamwak research was carried out under the auspices of the  PNG National Museum , the  Manus Provincial Government  and the traditional owners of the Pamwak site, the people of Drabitou 2 village. Clayton Fredericksen  assisted with the excavation and analysed the stone material. Bernard Minol  is thanked for his support and interest in the project. For the Sasi site, excavated by  Wal Ambrose  in addition, we acknowledge Sungia for access to the site and the people of  Baun Village  for their support and assistance. That research was carried out as part of the  ANU-National Geographic Society Lapita Homeland Project , directed by  Jim Allen. With regard to work at  Matenkupkum on New Ireland , we wish to thank the  PNG National Museum  and the  National Research Institute  for approving our research in PNG and subsequent analyses.
Age and sexual prejudice Sexual stereotyping is most certainly not only varying across cultures, but also across different age groups. Sexual stereotyping is generally assumed to be higher in older populations, homosexuality being more accepted among the youth  (OECD, 2019). This has been explained by an "ageing effect", i. e. the fact that people become less accepting towards homosexuality when they grow older. Recent studies, however, suggest a cohort effect whereby younger generations are more liberal, and therefore more conducive to greater acceptance of sexual minorities. The withincohort study by  Andersen & Fetner (2008) , for instance, analyses the tolerance of homosexuality in Canada and the United States, using a dataset from the World Values Survey  (1981) (1982) (1983) (1984) (1985) (1986) (1987) (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000).
Sexual prejudice and sentiment analysis In order to analyse the sentiments and discrete emotions that surface in the experimentally elicited, spontaneous speech samples, we employ sentiment analysis techniques. Sentiment analysis is the automatic determination of emotion in text, comprising valence (i. e. , positivity, negativity or neutrality), emotions (e. g. , anger, joy, disgust), and other affectual states, using computer algorithms  (Mohammad, 2021). More generally, sentiment analysis can determine one's attitude towards a particular target or topic, i. e. one's evaluative judgment (positive/ negative), one's affectual attitude (e. g. , frustration, anger, etc. ) or one's emotional state  (Mohammad, 2021). Sentiment analysis is specially suited in order to explore our data and in particular sexual prejudice and its sociocultural basis in France.
It has been successfully applied before in studies aiming at a better understanding of social groups which focussed, for instance, on differences in emotions in language used by different social groups or on differences in language mentioning people from diverse backgrounds  (Grijalva et al. , 2014; Mohammad & Yang, 2011; Montero, Munezero, & Kakkonen, 2014). However, sentiment analysis comes with some challenges. The most important challenge is that language in use is complex and subtle, i. e. the meaning of a sentence or text (utterance meaning or pragmatic meaning) is not just simply the sum of the meanings of its parts (word or lexical meaning;  Mohammad, 2021). Moreover, language can be used creatively and in non-standard ways  (Mohammad, 2021). To provide one specific example from our dataset, homosexuality or homosexual is not simply a negative lexical item, as is assumed by default in common affect lexicons, but can be positively or negatively construed in context (e.
g. , homosexuals as friends), or lexical items might be modified, i. e. negated, amplified or intensified (e. g. , I am not disgusted/ very disgusted). These challenges can, however, be met by using algorithms that have been trained on a sufficiently large and high-quality dataset, or by manual analyses. While automatic analyses are error prone, manual analyses are costly and time-consuming  (Abdaoui, Azé & Bringay, 2017). Another challenge to the analysis of the French dataset is that work on sentiment analysis in languages other than English are rare, and therefore, less accurate  (Abdaoui, Azé & Bringay, 2017). However recently, there has been an increasing interest in French sentiment analysis and efforts to leverage the resources available in English for sentiment analysis in French and other resource-poor languages  (Kooli & Pigneul, 2018; Paroubek et al.
Procedure & measures The participants were randomly presented with one of eight pictures evoking homosexuality. Half of the pictures showed two males, the other half two females intimately involved (e. g. , french-kissing). Each participant was asked to describe freely what the picture evoked to them without prior knowledge of the study aim. Subsequently, the data was manually transcribed. Data analysis was performed on a subset of the speech samples wedding sentiment analysis and discourse analytical procedures. This complementary approach allowed us to retrieve subjective information with respect to homosexuality and to perform a context-sensitive analysis, e. g. , to retrieve negative and positive polarity items (word-level polarity) conveying negative or positive meaning in their linguistic context (sentence-level polarity).
The subsample analysed (N=135) comprised speech samples by the heterosexual participants of the complete dataset, participants of French (40 women, 24 men) and Maghrebian origin (33 women, 38 men; cf. Table  1B ). We focused on heterosexual speech samples because of two reasons: First, we assumed that sexual prejudice would be most visible in populations that endorse heteronormative belief systems and norms of masculinity and femininity to a high degree, i. e. heterosexual men and women. Although, of course, gay individuals as well might hold prejudiced attitudes towards themselves (e. g. , in form of internalized homophobia;  Rosser et al. , 2008). Secondly, our sexual minority sample was unbalanced across French and Maghrebian participants. Maghrebian origin was defined as having at least one Maghrebian, i.
Statistical analysis All statistical analyses were performed using R (version 4. 0. 5; R Development Core Team 2021) and binomial GLMMs ('lme4' package version 1. 1-27;  Bates et al. , 2021; Levshina, 2015 ; estimation method: maximum likelihood; link function: inverse logit; random intercept model). This allowed us to analyse the number of negative and positive polarity items (separately for adjectives, verbs and nouns) relative to the total number of words per speech sample as response variable, using cultural background, sex, the interaction of cultural background and sex, age, and the sex of the kissing couples on the picture prompts as explanatory variables. An observation-level-random effect was included in order to account for overdispersion. For the analysis of conveyed emotions, we calculated the number of affect lexemes per emotion category (joy, sadness, fear, anger, disgust) relative to the total number of affect lexemes per speech sample as response variable, using emotion category, gender and cultural background, as well as the interactions between cultural background and sex, emotion category and sex as well as emotion category and cultural background as explanatory variables.
Study 1a To test our first hypothesis, we conducted a mixed model analysis using the lme4 package for the ANOVA (R package) and the r2glmm package for the computation of the effect size with the nsj method. We treated target status, facet, and their interaction as fixed effects. We included both traits and participants as random intercepts along with the relevant random slopes. There was a significant effect of target status (b = 0. 94, t(146) = 9. 01, p <. 001, Rsq =. 13) and a significant interaction (b = -0. 88, t(146) = -6. 88, p <. 001, Rsq =. 032). As predicted, the target status effect was larger on assertiveness (b = 1. 38, t(146) = 10. 92, p <. 001) than on ability (b = 0. 50, t(146) = 4. 25, p <. 001) (see Table  1a ). To examine our second prediction, we examined the means of participants' ratings of the two groups on the two facets.
METHOD Because we needed participants located at ranks 3, 4, 7, or 8 for Study 2 and at ranks 5 or 6 for Study 3, we relied on a single data collection and assigned participants to Study 2 or Study 3 depending on where they located themselves on the ladder. Again, we recruited American participants via Prolific Academic, who completed our online questionnaire (using Qualtrics) in exchange for $1. 36. A power analysis (PANGEA) indicated that we needed a minimum of 90 participants per study to reach a power of 99% with the effect size (Rsq = 0. 082) associated with our three-way interaction (see Study 1b). Based on the distributions observed in Studies 1a and 1b, and taking into account effect size overestimation and data loss, we collected 385 participants. We discarded 26 participants who located themselves at ranks 1, 2, 9, or 10.
RESULTS As before, we treated target status, facet, and judge status, and their interactions as fixed effects in a mixed model analysis using the lme4 package for the ANOVA (R package) and the r2glmm package for the computation of the effect size with the nsj method. We included both traits and participants as random intercepts with all possible random slopes. There was a significant effect of target status (b = 0. 98, t(202) = 10. 40, p <. 001, Rsq =. 132). As predicted, the target status by facet interaction was also significant (b = -1. 07, t(202) = -9. 43, p <. 001, Rsq =. 044), showing that the difference between the two targets was more pronounced on assertiveness than on ability. Confirming our second hypothesis, the target status by facet by judge status interaction also proved significant (b = 0.
Participants and Design As mentioned above, we conducted the data collection at once for Study 2 and Study 3, and the program directed 135 participants to Study 3 who had located themselves at ranks 5 or 6. We further removed 15 from the analyses because they failed the manipulation or attention checks  or used a consistent response pattern. The final sample comprised 123 participants (55. 8% women, M age = 29. 8). The design was a 2 (target status: high vs. low) × 2 (facet: assertiveness vs. ability) × 2 (judge status: high vs. low) mixed design, with the first two factors varying within participants and the third between them.
Prospective registration This study has been registered prospectively with the Core Outcome Measures in Effectiveness Trials (COMET) initiative; the registration number is 1586 and is available online (www. comet-initiative. org/ Studies/Details/1586). The study methods have been informed by reviewing previous core outcome sets in women's and newborn health  (Williamson et al. , 2012; Khalil et al. , 2017; Whitehouse et al. , 2017; Duffy et al. , 2018; Al Wattar et al. , 2020; Duffy et al. , 2020a; Doumouchtsis et al. , 2020; Ghai et al. , 2020; Kim et al. , 2020). Step 1: Identification of potential core outcomes (what outcomes have been reported before?) The Cochrane Central Register of Controlled Trials (CENTRAL) is a bibliographical database containing randomized trial reports identified by searching other bibliographical databases, including EMBASE, Medline and PubMed.
Ten research priorities were identified. The Core Outcome Measures for Infertility Trials initiative, which primarily focuses on female infertility. However, the challenges to be addressed in male infertility differ: the nature of male infertility trials means they have up to three potential participants, namely a male and female participant and their offspring, all with potential outcomes to be reported. To address this challenge, the development of a unique male infertility core outcome set is required. title and abstracts will be performed in duplicate and disagreements will be resolved by discussion with a third reviewer. No language restrictions will be applied, and translations will be sought for non-English language reports. Full-text reports will be reviewed for eligible trials. Data will be extracted in duplicate using a standardized and piloted data extraction proforma recording study characteristics and primary and secondary outcome reporting.
Participants will be invited to discuss the ordering of the outcomes within each category, considering contextual information, including the relative importance of individual outcomes, feasibility to collect the outcome data in future trials and the availability of suitable definitions and measurement instruments. They will be encouraged to reformulate outcomes to improve clarity or comprehension. The discussion among participants will focus on ranking outcomes to be considered for inclusion in the final core outcome set and the outcomes where no consensus existed. During the discussion, outcomes can be moved between categories. Finally, the core outcomes will be agreed upon. Step 3: Identification and standardization of core outcome measures (ensuring outcome measures are fit for purpose) Once core outcomes have been agreed upon by the Delphi consensus, we will determine how these outcomes should be defined and measured.
, 2020d (Duffy et al. , , 2021c)). These definitions were developed using formal consensus development methods for individual core outcomes; however, a core outcome set specifically for male infertility research is required. This additional congruence across future male infertility trials should ensure secondary research can be undertaken prospectively, efficiently and harmoniously  (Duffy et al. , 2020d (Duffy et al. , , 2021c)). This standardization will be supported by developing a freely available electronic case report form and data repository (COMMIT-Collection), which future researchers will be encouraged to use for data collection. The Core Outcome in Women's Health (CROWN) initiative, supported by 84 specialty journals, including the Cochrane Gynaecology and Fertility Group, Fertility and Sterility and Human Reproduction, has resolved to implement this male infertility core outcome set  (Khan, 2016).
While we cannot directly measure the psychological preferences of individuals, and the moral, emotional, and cognitive tendencies of people from the past, we can use cultural artifacts as a window to the zeitgeist of societies in particular historical periods. At present, an increasing number of digitized texts spanning several centuries is available for a computerized analysis. In addition, developments form historical economics have enabled increasingly precise estimations of sociodemographic realities from the past. Crossing these datasets offer a powerful tool to test how the environment changes psychology and vice versa. However, designing the appropriate proxies of relevant psychological constructs is not trivial. The gold standard to measure psychological constructs in modern texts -Linguistic Inquiry and Word Count (LIWC) -has been validated by psychometric experimentation with modern participants.
INTRODUCTION One of the core missions of social and political sciences is the study of how individual beliefs and values are formed, how they change, and how the interaction between individuals impacts the dynamics of society and of political institutions. Population-wide surveys and opinion polls are one of the main traditional tools to gather information on values and beliefs  (European Values Study, 2021; WVS Database, 2021). These data can be used to uncover the relationships between social values, political institutions, group behavior, and the social-economic environment with rigorous quantitative tools (e. g. ,  Hansen et al. , 2018; Talavera et al. , 2018; Ruck et al. , 2019; Knechel and Mintchik, 2020). More recently, an explosion in social media data and the advent of computational social sciences have also allowed a quasi-real-time characterization of beliefs, values, and emotions of social media users, which constitute large segments of the population  (Lazer et al.
, 2009 (Lazer et al. , , 2020;; Giles, 2012; Mäntylä et al. , 2018; Yadav and Vishwakarma, 2020). While useful, social media and polling data are limited in their temporal scopes. Facebook and Twitter were only founded after 2004, and social media data are not extensive before this period. Moreover, while modern polling of electoral preferences has been conducted in the US since the first half of the 20th century, systematic surveys of values and attitudes are relatively recent (since the 1980's)  (European Values Study, 2021; WVS Database, 2021). Thus, different forms of data are required to reconstruct longer time series of relevant psychological and cultural dynamics throughout history. Fortunately, a second revolution occurred within computational social sciences, particularly in the field of digital humanities, pertaining to the availability of digitized cultural data such as paintings  (Morin, 2013; Safra et al.
looseness since 1800  (Jackson et al. , 2019). Concomitantly to this revolution in digital humanities, there has been an improvement in the techniques of historical econometrics, improving socioeconomic estimates further into the past. The availability of these time series in systematic datasets  (Wrigley et al. , 1997; Maddison Project Database, 2018; Clio Infra|Reconstructing Global Inequality, 2019; Our World in Data, 2019)  enables the investigation of the relationship between psychological traits and socioeconomic trends. For instance, we can test how living standards in the early modern period (e. g. , GDPpc, life expectancy) influence the prevalence of prosocial attitudes in theater plays  (Martins and Baumard, 2020)  or whether they change art patrons' predispositions to be represented as dominant or trustworthy  (Safra et al.
The Challenge of Using Text Mining to Explore Historical Sentiments Within computational social sciences, text analysis is particularly popular. Customizable and sophisticated tools for automated Natural Language Processing (NLP) have been developed for widely used programming languages such as R  (Wild, 2021)  or Python (Natural Language Toolkit-NLTK 3. 6. 2 Documentation, 2021). NLP can be used not only to assess the frequency of words, or bags of words, but also to perform more complex sentiment  (Liu, 2020)  and topic analyses  ( Řehåřek and Sojka, 2010). Being highly customizable, these tools can be used to describe and detect overall patterns of language use, how they change, and how they are distributed across groups  (Serrano-Guerrero et al. , 2015; Karjus et al. , 2018 Karjus et al.