dataesr/scientific-paragraphs-categorization huggingface dataset:
Prediction We based our work on the canonical labels defined for each text in the corpus. These were then used as ground truth for our binary classification. Two distinct experiments were conducted for the two canonical scales retained. The automatic classification of texts is a well studied problem in statistics. One family of models, Support Vector Machines (SVM), is of particular interest here because it obtains good results (Yu) when classifying literary texts, and has the advantage of reducing the risk of over-fitting. In this paper, we used the family of SVMs developed by the Scikit-learn team since 2011  (Pedregosa et al. ) , and more specifically the SVC estimator. We ran our model in a basic 5-fold cross-validation set up. The dataset is split into 5 consecutive folds and each fold was used once as a validation while the 4 remaining folds formed the training set.
Given the nature of the features used in authorship attribution, we wanted to avoid over-fitting on an author's writing style. To do so, we implemented Scikit-learn's Group strategy. All works by the same author (group) were placed in the same fold; thus, each group will appeared exactly once in the test set across all folds. In this manner the model cannot cheat and recognize the same idiolectal information in both the training and the test sets. See appendix A. 3 for the detailed prediction setup, in particular for how we handled the baselines. Data imbalance was especially challenging at the novel scale, given that our canonical sample represents only 10% of the dataset. Since SVM models are quite sensitive to such imbalanced classes, we re-balanced the classes before implementing the classification by taking the 306 canonical novels and randomly adding 306 non-canonical novels (50% canon, 50% non_canon).
The model has access to a larger volume of training data from this particular timeframe, leading to a specialization in this era. Nonetheless, this over-fitting does not appear to hinder the model's performance, as it continues to perform well. The red non-linear regression is fitted on the predicted probability for each text to be canonical. This prediction is retrieved from the test set, meaning that the model has seen neither the novel in question nor the writer's other works. There is a huge increase in this probability over time, from 0. 2 to 0. 6 while it should be around 0. 5 since our dataset is balanced. This result is discussed in the next section.
Subsequent investigations are necessary to comprehensively grasp the nuances embedded in the literary canon. A future approach would involve obtaining metadata chronologically, as reception evolves. Further possibilities encompass dissecting the canon through various literary field agents such as editions, textbooks, school, academic prestige, and literary journals. Additionally, incorporating advanced algorithmic techniques in natural language processing, such as word or paragraph vectors, topic embeddings, and transformers, could enhance the analysis of more intricate textual attributes. Peer reviewers: Rachel Buurma, Yann Ryan Dataverse repository: https://doi. org/10. 7910/DVN/K4AAAF As a baseline, a random approach was adopted to ensure that the statistical model detected textual differences associated with the metadata rather than artificially managing to separate the two classes.
This strategy essentially acts as a "cheat code" for the model, facilitating author attribution instead of focusing solely on canonicity. This observation carries intriguing implications, potentially underpinning the argument that the canon might be construed as an amalgamation of distinct authorial styles operating within specific subgenres. PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where most of the variation in the data can be described and represented in two dimensions to visually identify clusters of closely related data points. In our experiment, we projected all the works of the same author on a single plane to be able to compare the works, using only the 100 most frequent words.
Acknowledgments Jean Barré 's PhD is supported by the  EUR (Ecole Universitaire de Recherche)   Translitterae (programme "Investissements d'avenir "  ANR-10-IDEX-0001-02 PSL  and  ANR-17-EURE-0025 ). This research was conducted as part of a Master's thesis in digital humanities at the  École nationale des Chartes. We would like to extend our gratitude to the entire teaching and administrative team, without whom this article would not have been possible.
We study how firm premia influence the gender wage gap for 21 European countries. We use a quadrennial harmonized matched employer-employee dataset to estimate gender-specific firm premia. Subsequently, we decompose the firm-specific wage premia differential into withinand between-firm components. On average, the former accounts mainly for the decline in the pay gap between 2002 and 2014. We pay particular attention to the development of each component by age group, and find that the between-firm component is associated with an increase in the gender pay gap over age. The decomposition of firm premia allows us to investigate how institutional settings relate to each component. We associate the within-firm component with collective bargaining at the national and firm levels, and the between-firm component with family policies.
(2018)  and  Masso et al. (2022)  investigate the role of motherhood. However, especially compared to previous work on the role of motherhood, we can exploit differences in family policy across Europe to assess which policies are related to the gender wage gap across age. The paper is structured as follows. Section II introduces the matched employer-employee dataset and explains our subsample selection procedure. Section III discusses and explains the estimation of the firm fixed effects, their normalization, the decomposition into the within-and between-firm components, and how we relate each component to institutional settings. Section IV presents the results, and Section V concludes.
A European matched employer-employee dataset The Structure of Earnings Survey (SES) is a harmonized quadrennial survey of majority of European countries. 2 Its key advantage is the provision of information on both sides of a match in the labour market. Further, the information, in particular for earnings, in which we are mainly interested for estimating the gender wage gap, are harmonized across all countries. The SES takes place every four years, and we use the waves from 2002 until 2014. 3 In a given year, the majority of countries conduct the survey in the month of October because it is considered the most representative as it is least affected by absences due to annual leave or public holidays. National Statistical Institutes collect the data for the SES in a two-stage procedure. First, a sample of local units is drawn, and then a random sample of employees is drawn from the chosen local units.
Only local units of enterprises with more than 10 employees are drawn. 4 Similar to  Abowd et al. (1999) , we use a subset of the full workforce. One objective of the SES is to analyse gender wage gaps in Europe. Eurostat (2009)  provides an introduction to which econometric methods can be used to evaluate the gender wage gap based on the SES. Rubery et al. (2002)  argue that it is difficult to find a suitable database to measure gender pay gaps across Europe. However, they argue that the main problems of the SES are the infrequency of the surveys and the omission of the public sector from the database. 5 Examples of previous work analysing gender pay gaps using the SES include  Boll and Lagemann (2019)  and  Leythienne and Ronkowski (2018). Both studies decompose the gender wage gap into observables and unobservables, and investigate the relative importance of observable characteristics to the pay gap.
6  On the worker side, we use explicitly information on gender, age and education levels provided by the SES. One drawback of the SES is that both age and education are grouped into categories instead of providing detailed information, namely age into five categories and education into three. 7 The five age bands are  20-29, 30-39, 40-49, 50-59 and 60-64. With respect to education, we differentiate between primary, secondary and tertiary education. We also exploit information on tenure, as this is the variable closest to experience in our dataset.
European policy indicators In order to link the within-and between-firm components of a firm's contributions to the gender pay gap at the national level, we use two main sources to measure national institutional settings. We exploit information on the centralization and organization of multi-level bargaining from the Institutional Characteristics of Trade Unions, Wage Setting, State Intervention and Social Pacts (ICTWSS)  (Visser 2019). Specifically, we link the within-firm component with the predominant level at which wage bargaining takes place, the combination of levels at which bargaining takes place, and the centralization of wage bargaining. From the OECD Family Database, we extract information on expenditure on families as a percentage of GDP, parental leave policies, and enrolment of young children in daycare facilities.
Observations are weighted by their sample weights provided by the SES. The lack of the panel dimension in the SES means that we cannot capture unobserved individual heterogeneity. Recent single-country studies examining how firms contribute to the gender wage gap apply the multiway fixed effects approach pioneered by  Abowd et al. (1999). 10 Given the data constraints, we follow implicitly  Barth et al. (2016)  and include a vector of firm effects by gender in the second step. The main drawback of not controlling for unobserved individual heterogeneity is that we could overestimate the between-firm component. In order to alleviate these concerns, we restrict the sample to firms where we observe at least three men and three women. In the second step, we explain the residual wage component  ict with firm fixed effects.
Central wage bargaining and the within-firm wage gap We start investigating the relationship between the within-firm component of the gender wage gap and wage bargaining institutions at the aggregate level (country-year pair). Due to the limited number of observations, we include only one explanatory variable at a time. The estimation equation takes on the form Within G ct = 𝛽 0 + 𝛽 G 1 WageBargaining ct + 𝛾 t + 𝜉 G ct , with G ∈ {M, F}, (6) where Within G ct represents the first terms of equations (  3 ) and (  4 ), respectively. The aggregated firm component, evaluated at either the male distribution or the female distribution of jobs in country c in survey year t, is explained by wage-bargaining indicators from the ICTWSS database, and year fixed effects ( t ). We assume a negative sign for  G as more centralized wage bargaining compresses the wage structure.
CONCLUSION We investigate the contribution of gender-specific firm premia to the gender wage gap for the majority of European countries. We make use of four waves of the Structure of Earnings Survey, a harmonized matched employer-employee dataset. Exploiting the decomposition framework of  Card et al. (2016) , we estimate the firm's contribution to the gender pay gap, and decompose this contribution into within-and between-firm components. We also examine differences in these components across five age groups. Finally, we link each component to different institutional settings. First, we explore the role of wage bargaining centralization for the within-firm component at the national and firm levels, as the within-firm gap likely relates to some form of discrimination. Second, we examine the role of family policies for the between-firm component across the lifecycle, as it related to family formation and non-pecuniary remuneration that family formation often implies.
ACKNOWLEDGMENTS We thank  Cyrille Schwellnus ,  Rafael Veiel ,  Fadi Hassan ,  Davide Romelli ,  Paul Scanlon  and  Guy Michaels  for helpful comments and discussions. Further, we thank participants at the  Irish Economic Association conference  and the Verein fuer Socialpolitik conference for valuable feedback. Jan-Luca Hennig  wants to thank the  Grattan Scholars programme at Trinity College Dublin  for financial support as this paper was part of the thesis. The project leading to this publication has also received funding from the  French government  under the 'France 2030' investment plan managed by the  French National Research Agency  ( ANR-17-EURE-0020 ) and from the  Excellence Initiative of Aix-Marseille University  (A*MIDEX).
g. Taylor and O' Brien, 2017; Campbell et al. , 2019)  to understanding labour in this part of the economy. Concerning quantitative analysis, we make use of Higher Education Statistical Data (HESA). We use data from the 2015/16 and 2016/17 Destinations of Leavers from Higher Education (DLHE) undergraduate cohorts surveyed 6 months after graduation in January 2017 and 2018, respectively. These were the last two DLHE cohorts before the switch to the Graduate Outcomes survey  Industry and Higher Education 0(0) for a detailed listing of subjects). Among joint honour students, we define theatre and performance graduates as those with at least 50% of their degree in theatre and performance subjects (and likewise for other creative subjects, at least 50% of their degree in other creative subjects).
In exploring the transition from study to work, we defined 'performance occupations' as those with a four-digit SOC2010 code of 3413, and other creative occupations were those non-performance occupations among the creative occupations as adopted in previous papers  (Faggian et al. , 2013). Educational occupations, more specifically, were defined as those four-digit SOC2010 codes starting with 231. We split noncreative occupations into graduate and non-graduate levels using the typology of  Elias and Purcell (2013). ( Quantitative data is complemented by in-depth semistructured qualitative interviews conducted online between December 2021 and November 2022 to gain a more detailed insight into the experience of people currently working in the sector. The qualitative sample is composed of 21 theatre actors and actresses 2 we reached through different means: four of them through snowballing (from three different interviewees), the contacts of 15 of them were found through online searches (in particular on Mandy, one LinkedIn-like platform for TV and film and theatre workers), two of them expressed the interest of being interviewed over one RSVP we shared through our personal social networks' contacts.
ORCID iDs Manfredi de Bernard  https://orcid. org/0000-0003-4792-8490 Roberta Comunian  https://orcid. org/0000-0001-8784-8765 Notes 1. We follow HESA's rules for data presentation: all numbers are rounded to the nearest multiple of 5 (Any number lower than 2. 5 is rounded to 0 and halves are always rounded upwards (e. g. 2. 5 is rounded to 5). Percentages are displayed to 0 decimal places. Percentages based on fewer than 22. 5 individuals are suppressed. Averages based on 7 or fewer individuals are suppressed. 2. The resulting sample composition included 11 male and 10 female participants. Ethnically, 17 identified were white, and 4 as black, Asian, and minority ethnic. Five had been in the sector between 3 and 11 years (early career), while 16 had more than 11 years of experience (mid-to established career).
Acknowledgements A preliminary version of this paper was presented at the  European Academy of Management (EURAM)  2023 Conference, Trinity Business School, Dublin, Ireland, 14-16 June, and at the  British Academy of Management (BAM)  2023 Conference,  University of Sussex Business School , Brighton, UK, 4-6 September. We are deeply thankful to the interviewees, who generously shared their time and experience with us, and whose participation made the project possible. Finally, we acknowledge the PhD Research Visiting period (May 2023) of the co-author  M. de Bernard  at  ESSCA School of Management, France : we advanced on revisions, and we finalized the paper during face-to-face exchanges and work meetings at  ESSCA School of Management Paris Campus. Dent T, Comunian R, Conor B, et al. (2020) Creative and Cultural Workforce in Europe Statistics Report.
The role of vocabulary in the SVR model should be examined as a separate factor. Moreover, by including a distinction between depth and breadth of vocabulary, we should be able to better understand its contribution to RC. Finally, although the SVR model is simple, it can guide the classification of reading profiles (see  Nation, 2019). It is clear that the SVR model remains a good framework for explaining RC. Despite this, all the results of this and other studies suggest that vocabulary needs to be integrated and evaluated if we are to gain a better understanding of reading ability. The frequencies of the words in each text were controlled with the French MANULEX database  (Lété, Sprenger-Charolles & Colé, 2004). Texts A and C, on the one hand, and B and D, on the other, were matched for difficulty using the Automated Reading Index and Gunning Fog Index  (Mailloux, Johnson, Fisher & Pettibone, 1995) , which takes account of the length of words and sentences.
However, this negative effect is more pronounced in lower-middle income countries and Africa. Some studies point out that the corruption-innovation relationship could be affected by some moderated variables. For example,  Wellalage et al. (2020)  examined whether gender matters. The authors used a sample of firms located in Latin American countries. The findings indicate that compared to their male counterparts, on average, women receive a greater payoff from bribery. Karaman Kabadurmus and Sylwester (2020) investigated the role of competition in the corruption-innovation relationship using firm-level data from the 2012-2016 business environment enterprise performance surveys. Their results show that all measures of bribery are positively associated with innovation especially for firms reporting many competitors and obstacles.
To measure an economy's openness, we use the sum of total exports and imports of goods and services as a percentage of the GDP to reflect the degree of openness to international trade. Trade openness is an important variable when studying the impact of importing innovation. We also employ information that reflects the connectivity of the population to Internet services, and this variable is also important in the context of innovation  (Björk and Magnusson, 2009; OECD, 2012; UNCTAD, 2018). The main variable in this study is imports of high technology. Finally, we use a corruption index, ranging from zero to six, where a lower value implies higher corruption. Data on corruption was extracted from the International Country Risk Guide (ICRG). Table  1  displays the definitions of all these variables as well as their sources.
d. -c). The European University of the Seas Alliance was formed between six universities in 2019: University of Cádiz (Spain), University of Western Brittany in Brest (France), University of Kiel (Germany), University of Gdańsk (Poland), University of Split (Croatia) and University of Malta (Malta). One of the key activities planned by the SEA-EU Alliance is to transform the involved universities through "sharing educational programs, analysis, policies, procedures, services, databases, infrastructure, research networks, and governance" (SEA-EU -The European University of the  Seas, n. d. ). The community of more than 10,400 researchers is a particular challenge for establishing ethical principles. One of the goals of the Alliance is to foster open educational resources, open science, and open data to communities and the world at large to expand the SEA-EU university model beyond borders.
Expenditure and number of grants from European Commission programs in Croatia When analysing research infrastructure (gross expenditure on research and development and its distribution across sectors  (private, high-education, state, etc. ) , number of grants from EC programs and total funding in Euros, the gross expenditure was €76. 231,740, with 21 signed grants receiving €121. 9 million in the EU's H2020 program, as well as 8 H2020 ERC grants receiving €8. 87 million.
Bodies and processes for research integrity at the SEA-EU university member A Non-Interventional Scientific Research Ethics Committee  [CERNI in French]  was set up at a regional level of "Brittany University Alliance" (AUB, 2022). An expert is designated to review methodology, data protection and human respect. The evaluation is sent to the Bureau to give an assessment whether positive or with minor/ major modifications. Modifications are to be sent within one month before resubmission. Final assessment is given within 15 days after resubmission. If the project falls into the interventional research scope, the CERNI transmits the case to the Human Protection Committee. A positive evaluation of a project review by the CERNI indicated that it complies with the ethics rules.
Key actors are universities, research, and scientific institutes, as well as commercial and non-profit companies of different sizes. Funding for research comes mainly from the state budget in the form of statutory funding and grants. The former is mainly allocated to the institutions by the Ministry of Education and Science, depending on the results of the national evaluation carried out every 4 years. Grants are offered through open calls by agencies subordinate to the Minister of Science and by other non-governmental institutions and private sector. R&D units are also largely supported by the European Structural Funds and Framework Programmes such as Horizon 2020 (2014-2020) or Horizon Europe (2021-2027). Bodies for research ethics and research integrity and scope of their action Several institutions located in Poland and abroad support research and development activities and help the government and its agencies to create a research and innovation strategy.
Purpose -This paper analyzes individual subjective well-being using a survey database from the Strasbourg metropolitan development council (France). The authors focus on the effects of externalities generated by public services (transport, culture and sport), environmental quality and feeling of security in the Strasbourg metropolitan area (Eurom etropole de Strasbourg, EMS). Results show that EMS specificities (public facilities, environmental quality, safety and security) and individual features like opportunities to laugh or live with children significantly influence individual well-being. These findings are robust when using three subjective measures: feeling of well-being, environmental satisfaction and social life satisfaction. The authors also show that income may affect the perceived well-being of individuals belonging to a low-income group, while individuals belonging to a high-income group tend to be unsatisfied with environmental quality but satisfied Strasbourg metropolitan area.
It is shown that the loss of health facilities and local shops (i. e. public and private facilities) appears to be the most strongly correlated with French dissatisfaction. Some other studies have focused on external factors, such as economic and environmental risks, geographical location and environmental quality, as potential determinants of individual well-being (e. g. ,  Welsch, 2006; Ferrer-i-Carbonell & Gowdy, 2007; Brereton et al. , 2008; Rehdanz & Maddison, 2005 , 2008; Pham et al. , 2019). Using data from the German Socio-Economic Panel,  Rehdanz and Maddison (2008)  show that local air pollution and noise levels diminish individual well-being. Concerning Ferrer-i-Carbonell and Gowdy (2007), they examine the link between individual well-being and individual environmental attitudes using data from the BHPS.