dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, we aim to propose a novel research framework integrating deep learning techniques for exploring the relationship between digital CSR communications and firm performance. Drawing upon agenda-setting theory, we hypothesize that digital CSR communications contribute to firms' financial performance due to the agenda-setting effect. Meanwhile, we also aim to uncover the role of stakeholder engagement on social media in the relationship between digital CSR communications and financial performance. To accomplish the research objectives above, we collected social media (i. e. , WeChat) and secondary financial data of 65 of the Top 500 Chinese A-share listed manufacturing companies from 2015 to 2019. Then, we utilized tweets data with long text forms to train the cutting-edge deep learning models (i.
Data collection and processing In this study, our sample firms contain 65 of the Top 500 Chinese Ashare listed companies 1 in the manufacturing sector from 2015 to 2019, all of which have been implementing digital CSR communications on the WeChat Subscription Platform through their official accounts. First, Chinese A-share listed manufacturing enterprises need to incorporate CSR communications into their social media strategies, as these companies are more likely to inflict pollution, excessive consumption of resources, and other irresponsible behavior  [30]. Second, we chose the WeChat Subscription Platform (WSP) as the communication channel on social media, as WSP has advantages of providing both detailed tweets with long-form texts and a much more massive user base for companies to engage with  [59, 60].
Therefore, we conducted a thorough manual identification and found that 65 of the Top 500 Chinese A-share listed companies opened official accounts on WSP. Finally, we set the observation period from 2015, given that WSP was launched in November 2014. We also closed the observation period before 2019 to eschew the complex impact of COVID-19 on the financial performance of manufacturing enterprises. In November 2021, we obtained unstructured textual data and secondary data of sample firms through multiple sources. Specifically, we employed a combination of crawler approaches to acquire the tweets of the sample firms posted on their WSP official accounts and obtained a total of 64,769 long-form tweets. We plan to apply deep learning-based natural language processing (NLP) models to identify the tweets that can be categorized into digital CSR communications and then yield stakeholder engagement data from relevant CSR tweets.
Variable measurements Digital CSR communications: This is a numeric variable DCSRC that we utilized the natural logarithm of identified and detected digital CSR communication tweets. Specifically, we manually labeled and applied the most accurate deep learning-based NLP model to detect sample firms' CSR-related tweets on their WeChat official accounts, with the former accounting for 20% and the latter for 80% of the whole 64,769 long-form tweets. After that, we aggregated detected CSR-relevant tweets into the yearly data to construct DCSRC. Stakeholder engagement: We construct the numeric variable of SEave by taking the natural logarithm of the average number of reads, likes, retweets, and comments embedded in the sample companies' digital CSR communication tweets as the proxy for stakeholder engagement.
Previous literature on stakeholder or customer engagement suggested that different social media interactions represent different engagement levels. For example, reads represent the consumption of corporate digital communication, likes to stand for support, and retweets and comments refer to the contribution and co-creation in firm communications. In light of this situation, we followed the approach of Dearden, et al. [72]  and Liu, et al. [66]  to average the four types mentioned above of stakeholder engagement. Specifically, we averaged all four engagement data of sample firms' each CSR-related tweets and then aggregated them into the yearly data to build SEave. CSR ratings: This study operationalizes CSRra by Hexun annual CSR ratings of Chinese listed companies. According to the annual reports of listed companies, Hexun rates the annual CSR performance of listed companies through five systematic evaluation indicators, i.
Akin to previous studies, this study inevitably has a few limitations that need to be addressed by future studies. Firstly, our sample firms are all from Chinese listed companies in A-share markets, which are mature and well-managed. Hence, future research can delve into small and medium enterprise samples and further scrutinize CSR communications' business value  [80]. Second, we merely focused on one social media platform in China to solicit and detect potential digital CSR communication tweets. However, more recent studies collected data from multiple social media platforms  [81] , which helps to improve the generality of research findings. Future research could enrich the collection channels of textual data to validate our findings. Third, there also exist limitations in the variable constructions and data processing.
Data extraction and quality assessment After the search, duplicate publications were removed. From October 13, 2021, to June 28, 2022, two reviewers (LC and JK) independently reviewed the titles and abstracts of all articles retrieved by the literature search. Then, if the title and abstract suggested that the study met the selection criteria, we reviewed the full texts of potentially eligible studies. The same two reviewers independently extracted data and study characteristics using a predefined data collection form: author, year of publication, title, host country, objective, study type, method, sampling strategy, sample size and sociodemographic characteristics, diagnostic tool and criteria, and primary outcomes. Data on the sex distribution of the samples were extracted separately for men and women, where possible.
Results These reference datasets were used to answer several questions regarding different carnivore species according to their taxonomic status (family, genus) and their body size: -Is there a difference between lower and upper canines? -Is there variation in morphology (L vs W) according to the degree of wear? -Are modern species appropriate reference material for fossil forms? -Are there differences between male and female individuals? -Is the overlap between taxa important? The distribution of upper and lower canine measurements has been plotted onto scatterplots (Fig. 5 ) and these evidence dimensional differences, except maybe for hyenids (Crocuta), the upper canines of which display a relative increase in the mesio-distal (L) measurement, from apex toward the base of the crown, over time.
Comparisons with modern puncture datasets Taphonomic literature is replete with morphometric data on tooth marks produced by modern species (e. g. ,  Dominguez-Rodrigo and Piqueras, 2003; Delaney-Rivera et al. , 2009; Andrés et al. , 2012 ; see introduction). These datasets developed for comparison with the fossil record are generally based on the principle of actualism. Indeed, these data of known origin are often considered reliable analogues and models for characterizing the role of predators in the fossil record. However, when all the metric data on punctures produced by modern carnivore species are considered, the range of variation in the tooth marks produced by each species shows a very large overlap (Fig. 9 ). Whether hyenids, canids or felids, apart from the extremes (the smallest or largest marks), it seems difficult to discriminate between species even within the same family.
When the range of variation of the canines is superimposed on these datasets, from the apex of the tooth up to interval 10 (see Methods section), discrimination at the species level seems even more delicate. This first comparison between punctures of known origin and our morphometric observations of modern carnivore canines underlines the difference between the dynamics of the trace (resulting from the progressive sinking of the tooth into the bone) and the tooth mark itself, which represents a fixed point in time.
Comparisons with fossil contexts Datasets of tooth marks from the modern faunal record constitute an essential documentary and comparative basis for the characterization of agents that played a role in archaeological and paleontological deposits. However, as previously seen, the variability or even the variables involved in the production of tooth marks are rarely considered. Hereafter, two fossil case studies are proposed for which the accumulating agents (felid, hyenid) were identified via multiproxy taphonomic approaches. The aim here is to test the relevance of the study of tooth marks alone by confronting these data with those presented here on morphometric variation in canines. The dental mark on bone is the result of a real dynamic process of tooth sinking into the bone. To represent this dynamic, the projections of the data presented here integrate the morphometric variation of the canine along the different measured intervals.
Fig. 10  shows the projected length/width ratio of the 84 Kromdraai punctures recorded compared to the range of variation in measured modern carnivore canines. For contextual relevance, only data from African species (jackal, wild dog, hyena, leopard and lion) has been included. The projection clearly shows the difficulty in recognizing a particular species from tooth mark morphometric data. Indeed, the Kromdraai data cover a large range of measurements which could correspond to all the carnivores considered here when compared to the canine data presented in this paper. There is a noticeable gap in measurement distribution between the small to medium-sized predatory species (jackal, wild dog) and the largest forms (leopard, lion, hyena), reflecting the physiological limit of the size of the animals and therefore of their canines.
Acknowledgements This paper is a contribution of the  IRN 0871   TaphEN CNRS-INE  and the  French-Kenyan CNRS   Programme Hubert Curien Pamoja  # 43102QA. The authors would like to thank  Francis Duranthon ,  Henri Cap  and  Guillaume Fleury  from the  MHN-Toulouse ,  Patricia Guillermin  and  Charlène Girard  from the  CPO , and  Ogeto Mwebi  from the  NMK  for granting access to the osteological collection under their care. The authors also thank  Bernhard Zipfel ,  Francis Thackeray  ( ESI, Wits ) and  José Braga  (UMR5288 CAGT), directors of the  Kromdraai Research Project , for allowing us to work on the fossil sample of Kromdraai. Finally, thanks to  Y. Fernandez Jalvo  for authorising the reproduction of Fig. 2, and to two anonymous reviewers for their remarks.
Moreover, the architects and the commissioner wished to collect data about the mechanical behaviour of several types of armatures which are sometimes still in place to reuse them in the building structure. Therefore 4 entire cramps, 4 cresting elements and 1 tie-rod iron sample were taken and machined into tensile tests pieces to measure their true characteristics. The analytical protocol and measurement conditions of these experiments are presented in L'Héritier et al. 2019  [20]. Metallographic examinations reveal that most structures are highly ferritic regardless of the armature type ( Fig. 5  ). Only a few cramps are slightly carburized but none can be considered as steel as they never exceed 0. 3%C in average. On the other hand, two nails are made of homogeneous steel close to the eutectoid with about 0.
Acknowledgements The authors would like to thank the  CNRS and Ministry of Culture  for the general organization and funding of this research as well as all the researchers involved in this program, especially the scientists of Metal Workgroup for very fruitful discussions. They also thank the  Service Régional de l'Archéologie, Conservation Régionale des Monuments Historiques, Etablissement Public Rebâtir Notre-Dame de Paris (RNDP)  and the  Architectes en Chef des Monuments Historiques  ( P. Villeneuve ,  P. Prunet ,  R. Fromont ) for the authorizations to access the monument and sample these armatures to perform this study. Many thanks to Jarnias company for their material help in sampling. The authors would also like to thank  Philippe Chevallier ,  Yvan Cotrebil  and  Rémy Pirès  from  ICMPE  for their assistance in the mechanical tests, the sample preparation and the analyses and experiments carried out in the OM and SEM.
Charcoal remains Charcoal remains were mostly recovered through flotation, while 21 fragments were handpicked from the wet sieving residue. Each charcoal fragment was fractured manually to provide transverse, tangential, and radial sections for taxonomic identification using a Nikon Labophot-2 bright/dark field incident light microscope with 50-500 × magnification at the University of Las Palmas de Gran Canaria. Botanical identification was performed using specialised plant anatomy atlases  (Fahn et al. , 1986; Parsapajouh et al. , 1987; Schweingrüber, 1976 Schweingrüber, , 1990) )  and online databases  (Inside Wood, 2004) , in addition to a reference collection of modern charred woody taxa. All the available charcoal samples were analysed. Photography and detailed observations of the anatomical and taphonomic features were carried out using a Zeiss EVO MA 15 scanning electron microscope (SEM) at the General Research Support Service (SEGAI), University of La Laguna.
Acknowledgements This research was funded by The  Gerda Henkel Stiftung  grant (n. AZ 10_V_17  and n. AZ 23/F/19 ), the  Fritz-Thyssen Foundation  grant awarded for the project " Pleistocene Hunter-Gatherer Lifeways and Population Dynamics in the Ararat (paleo-lake) Depression, Armenia ", and The  European Research Council  grant  N 948015 : " Investigating Pleistocene population dynamics in the Southern Caucasus " (awarded to AMB). Further support was provided by  Gfoeller Renaissance Foundation (USA)  and " Areni-1 Cave" Consortium  (" Areni-1 Cave" Scientific-Research Foundation (Armenia)  and the  Institute of Archaeology and Ethnography of the National Academy of Sciences of the Republic of Armenia , the  Gfoeller Renaissance Foundation , the  Leakey Foundation  and the  Leverhulme Trust-funded Palaeolithic Archaeology, Geochronology, and Environments of the Southern Caucasus (PAGES)  project ( RPG-2016-102 ).
Data are collected on both the initial exercise sheet and the second one, thereby forming a pre-and post-test for the activity. The implementation of the activity is left up to the teacher. Indeed, the objective here is to assess the impact of an activity of the same kind as those found in school textbooks; that is, an all-in-one tool designed to be utilized without the need for additional input. For the purpose of IRT analysis, all tasks and responses were binarized using a stringent criterion, assigning a value of 1 for complete success and 0 otherwise. Items within the first exercise sheet featuring standard mathematical tasks were designated as M1a, M1b, M2a, M2b, and so forth. Similarly, items involving historical aspects in the final exercise sheet were labeled as H1a, H1b, H2a, H2b, and so on.
Rasch Modeling & Item Selection The fit of Rasch model was checked by bootstrap using the dedicated function in the ltm package  (Rizopoulos, 2006). A p-value of 0. 56 (greater than 0. 05) for this model indicates a good overall fit. Items M2c and H5b are at the edge of the model, so we will avoid giving them a too significant weight in the analyses that follow. To allow for a comparison of students' abilities between the mathematics and history parts, the items were paired by difficulty level following the method suggested by  Wright (1993). Data from both tests are combined and analyzed together. Item calibrations are thus completely equated because they are all expressed at once on one common linear scale. One can then select pairs of items showing the same difficulty level. To constitute a pair, the difference in difficulty level between an M item and an H item must not exceed 0.
This will preserve key relationships in the data and RTM effect will remain across all simulated datasets. Relevant statistics is calculated for each permutation. According to all principles, we create 10,000 permutations of our dataset, select less proficient students, and calculate mean change between pre-and post-test (Figure  7 ). The overall RTM estimation by the permutation method is +0. 53 logit within a 95% confidence interval [0. 25; 0. 77]. The experimental value +0. 67 logit is not outside the confidence interval, so H0 cannot be rejected, which means that the observed effect cannot be attributed to the added value of the history-based approach. Corrected Cliff delta effect size is 0. 10 (negligible).
A hypothesis for further research is then that the introduction of a historical perspective induces a decorrelation of student results between their initial level in mathematics and that, which they show during this type of activity. This reset can be a positive point and a way to understand and statistically measure the expected epistemological disorientation ("dépaysement épistémologique" in French) effect claimed by part of the international community involved in the use of history in mathematics education  (Guillemette, 2018). In our dataset (paired items only), the per quartile analysis of RTM effect (R script available online, De Vittori, 2023) is ranging from +0. 52 logit for the first quartile to -0. 35 logit for the fourth (Figure  8  and Table  4 ). According to results, RTM effect is non-negligible and might prove a true epistemological disorientation.
Data We use the ERA5 reanalysis  [74]  as the climate data source, Red Eléctrica de España as the electricity and market price data source, and the Spanish Ministry for the Ecological Transition and the Demographic Challenge as the installed capacity source. The main data source for the different layers of suitability is the Spanish Instituto Geográfico Nacional de España (IGN). Excluding the natural protected spaces (that combine data from IGN and the Spanish Ministry for the Ecological Transition and the Demographic Challenge) and land use (CORINE Land Cover is derived from the homonymous European project, although this data is made available through IGN), all other suitability data has IGN as its original source. We conduct this analysis using hourly data for a 5-year period comprising the years 2016-2020, both included.
We use the ERA5 variables for wind at 100 m, temperature at 2 m, and surface shortwave downward radiation at surface. Wind energy generation is estimated from wind, and PV generation is calculated from radiation and temperature (which affects the efficiency), and both are translated to the corresponding capacity factors and calibrated using the observed REE electricity data on both generation sources. Observed hourly demand is used to fit a temperature-dependent function that relates demand to its base component and some additional terms related to heating and cooling systems. In addition to the calibration process, REE data is also used to estimate the 𝛼 coefficient under the assumption of a perfect market, using the relation of generation cost and market price and dispatchable generation.
Acknowledgments This research is framed within the  TRAMPAS  ( PID2020-113036RB-I00/AEI/ 10. 13039/501100011033 ) project funded by the  Spanish Agencia Estatal de Investigación (AEI)  from the  Spanish Ministerio de Ciencia e Innovación. Author A. Maimó-Far received funding from the  Spanish Ministerio de Ciencia Innovación y Universidades  ( FPU18/ 00520 ). This research made use of e4clim, an open source software, developed in the frame of the  Energy4Climate Interdisciplinary Center (E4C) of Institut Polytechnique de Paris and École des Ponts Paris-Tech  and supported by the  3rd Programme d'Investissements d'Avenir  [ ANR-18-EUR-0006-02 ] and by the  Foundation of École Polytechnique (Chaire Défis Technologiques pour une Énergie Responsable). The authors would like to thank  Joan Bauzà Llinàs  and the  Servei de SIG i Teledetecció de la Universitat de les Illes Balears  for providing the geographical data.
We tested this model on 220 subjects extracted from the preelection American dataset. Our findings reveal a notable distinction in its ability to account for individual responses, as evidenced by the mean squared error (MSE) metrics (mean(MSEci) = 0. 075, mean(MSEbayes) = 0. 093). This difference was statistically significant according to the Wilcoxon test, p= 2. 673e -2. Furthermore, the Bayesian information criterion (BIC) values corroborate these results, with BICci = -549 and BICbayes = -502.
Availability of data and material The anonymised dataset and the analyse codes will be available after the end of the data analysis in the open science framework: https://osf. io/chpfk/?view_only=0ba1ecd434ce427fb115a447cf36c2c1 with the DOI https://doi. org/10. 17605/OSF. IO/CHPFK 12. Declarations 12. 1.
After excluding these outliers, the remaining eleven ages range between 12. 5 ± 1. 2 ka and 25. 8 ± 2. 4 ka and reduced chi-squared test shows a much improved cluster: 𝜒 𝑅 2 = 6. 31. However, the variability of the remaining ages is 24. 1%, and with a 𝜒 𝑅 2 > 2, the dataset can be described as poorly-clustered  (Blomdin et al. , 2016). The arithmetic mean and the standard deviation for these eleven surface exposure ages is 18. 0 ± 4. 3 ka (Fig. 5b ), and it could represent the minimum deglaciation age of the study area, however geomorphological processes could have had large impact on the spread of exposure ages  (Heyman et al. , 2011).
Acknowledgements This work was supported by the  National Science Centre in Poland  [grant no. 2011/01/N/ST10/05880  to KT], the  Polish Ministry of Science and Higher Education  [grant no. N N306 316835  to WW] and funding from the  Faculty of Oceanography and Geography, University of Gdańsk. The ASTER AMS national facility (CEREGE, Aix-en-Provence) is supported by the  INSU/CNRS , the  ANR  through the " Projets thematiques d'excellence" program  for the " Equipements d'excellence "  ASTER-CEREGE  action and IRD. https://doi. org/10. 5194/tc-2023-117 Preprint. Discussion started: 14 August 2023 c Author(s) 2023. CC BY 4. 0  License.
Results Choices did not differ between the two counterbalanced Same Charity conditions, nor between the two Different Charities conditions (ps > 0. 472), indicating that participants did not generally prefer one charity to the other. We therefore collapsed the data in each condition. As predicted, participants were less likely to choose the self-beneficial option in the Same Charity condition (36. 0%), which had only one noncommon effect, than in the Different Charities condition (58. 6%), which had attributional ambiguity, Chi 2 (1) = 11. 29, p =. 001, V = 0. 23, 95% CI [0. 10, 0. 35] (see Table  1 ). For the personal preference question, 62% of all participants voted for the END Fund to receive the additional donation, while 60% picked the END Fund for the popularity question. To answer whether the effect of the Same vs.
The interviews were transcribed and analysed using a thematic analysis grid. We then selected the thematic elements that could be mapped based on the frequency with which they were mentioned by the intervie-wees. We integrated these elements into the Quantum geographic information system (QGIS) and the Inkscape computer-aided drawing (CAD) software. To gain additional clarity and perspective on the information obtained from the interviews, we consulted the grey literature (reports and websites of parks and CAUEs, park charters, and CAUE brochures on farm buildings). 8  Finally, photographs were taken to illustrate the dynamics described.