Humanities & Social Sciences

89 items

arXiv Sep 23, 2026
Using a purpose-built classifier, BlameBERT (macro-F1 0.80), to label roughly 4.94 million Danish parliamentary sentences from 1997 to 2026 and multilevel statistical models, the study finds a banana-shaped blame trajectory that declined to a low point around April 2016 before rising significantly and accelerating, with government status consistently dampening blame (termed political contrasting) and this effect moderated by ideology, such that right-wing parties show a stronger blame increase with ideological extremity, an interaction that intensified in recent years.
Using a purpose-built classifier, BlameBERT (macro-F1 0.80), to label roughly 4.94 million Danish parliamentary sentences from 1997 to 2026 and multilevel statistical models, the study finds a banana-shaped blame trajectory that declined to a low point around April 2016 before rising significantly and accelerating, with government status consistently dampening blame (termed political contrasting) and this effect moderated by ideology, such that right-wing parties show a stronger blame increase with ideological extremity, an interaction that intensified in recent years.
Nearly five million Danish parliament sentences show blame falling until 2016 then climbing, with the sharpest rise among extreme right-wing parties
Using a purpose-built classifier, BlameBERT (macro-F1 0.80), to label roughly 4.94 million Danish parliamentary sentences from 1997 to 2026 and multilevel statistical models, the study finds a banana-shaped blame trajectory that declined to a low point around April 2016 before rising significantly and accelerating, with government status consistently dampening blame (termed political contrasting) and this effect moderated by ideology, such that right-wing parties show a stronger blame increase with ideological extremity, an interaction that intensified in recent years.
Using a purpose-built classifier, BlameBERT (macro-F1 0.80), to label roughly 4.94 million Danish parliamentary sentences from 1997 to 2026 and multilevel statistical models, the study finds a banana-shaped blame trajectory that declined to a low point around April 2016 before rising significantly and accelerating, with government status consistently dampening blame (termed political contrasting) and this effect moderated by ideology, such that right-wing parties show a stronger blame increase with ideological extremity, an interaction that intensified in recent years.
发表出处待核验 Sep 22, 2026
This chapter argues that the literary historiography of science (LH) offers its own fresh perspectives on the history of science, using the history of evolutionary theory as its case: it shows how LH scholarship and methods have been integral to the re-evaluation of Charles Darwin's significance by both historians of science and literary scholars, and it models a history of evolutionary theory extending well beyond the conventional "Modern Synthesis" of Darwinian natural selection and Mendelian genetics by showing how Olaf Stapledon's evolutionary science fiction has shaped disciplines from astrobiology and Earth system science to the theorisation of Artificial Intelligence.
This chapter argues that the literary historiography of science (LH) offers its own fresh perspectives on the history of science, using the history of evolutionary theory as its case: it shows how LH scholarship and methods have been integral to the re-evaluation of Charles Darwin's significance by both historians of science and literary scholars, and it models a history of evolutionary theory extending well beyond the conventional "Modern Synthesis" of Darwinian natural selection and Mendelian genetics by showing how Olaf Stapledon's evolutionary science fiction has shaped disciplines from astrobiology and Earth system science to the theorisation of Artificial Intelligence.
Literary History of Science: From Reassessing Darwin to Stapledon's Evolutionary Science Fiction
This chapter argues that the literary historiography of science (LH) offers its own fresh perspectives on the history of science, using the history of evolutionary theory as its case: it shows how LH scholarship and methods have been integral to the re-evaluation of Charles Darwin's significance by both historians of science and literary scholars, and it models a history of evolutionary theory extending well beyond the conventional "Modern Synthesis" of Darwinian natural selection and Mendelian genetics by showing how Olaf Stapledon's evolutionary science fiction has shaped disciplines from astrobiology and Earth system science to the theorisation of Artificial Intelligence.
This chapter argues that the literary historiography of science (LH) offers its own fresh perspectives on the history of science, using the history of evolutionary theory as its case: it shows how LH scholarship and methods have been integral to the re-evaluation of Charles Darwin's significance by both historians of science and literary scholars, and it models a history of evolutionary theory extending well beyond the conventional "Modern Synthesis" of Darwinian natural selection and Mendelian genetics by showing how Olaf Stapledon's evolutionary science fiction has shaped disciplines from astrobiology and Earth system science to the theorisation of Artificial Intelligence.
Journal of Artificial Intelligence General science (JAIGS) ISSN 3006-4023 Sep 22, 2026
This conceptual article argues that although enterprise performance management has moved from static periodic reporting to continuous real-time monitoring, finance leaders still obtain abundant data without faster or firmer decisions, locating the binding constraint in scarce managerial attention and ambiguous decision authority, and proposing a three-layer architecture of performance, attention, and authority, a five-dimension Management Attention Test (materiality, persistence, confidence, decision ability, ownership), a separation of analytical from decision authority, and governance for responsible AI use in finance, concluding that disciplined filtering and accountability serve organizations better than more AI dashboards, with AI as an analytical partner rather than a decision substi
This conceptual article argues that although enterprise performance management has moved from static periodic reporting to continuous real-time monitoring, finance leaders still obtain abundant data without faster or firmer decisions, locating the binding constraint in scarce managerial attention and ambiguous decision authority, and proposing a three-layer architecture of performance, attention, and authority, a five-dimension Management Attention Test (materiality, persistence, confidence, decision ability, ownership), a separation of analytical from decision authority, and governance for responsible AI use in finance, concluding that disciplined filtering and accountability serve organizations better than more AI dashboards, with AI as an analytical partner rather than a decision substi
Performance, Attention, and Authority: Rethinking AI-Enabled Enterprise Performance Management
This conceptual article argues that although enterprise performance management has moved from static periodic reporting to continuous real-time monitoring, finance leaders still obtain abundant data without faster or firmer decisions, locating the binding constraint in scarce managerial attention and ambiguous decision authority, and proposing a three-layer architecture of performance, attention, and authority, a five-dimension Management Attention Test (materiality, persistence, confidence, decision ability, ownership), a separation of analytical from decision authority, and governance for responsible AI use in finance, concluding that disciplined filtering and accountability serve organizations better than more AI dashboards, with AI as an analytical partner rather than a decision substi
This conceptual article argues that although enterprise performance management has moved from static periodic reporting to continuous real-time monitoring, finance leaders still obtain abundant data without faster or firmer decisions, locating the binding constraint in scarce managerial attention and ambiguous decision authority, and proposing a three-layer architecture of performance, attention, and authority, a five-dimension Management Attention Test (materiality, persistence, confidence, decision ability, ownership), a separation of analytical from decision authority, and governance for responsible AI use in finance, concluding that disciplined filtering and accountability serve organizations better than more AI dashboards, with AI as an analytical partner rather than a decision substi
arXiv Sep 22, 2026
Researchers from Peking University, the University of the Chinese Academy of Sciences, and Zhongguancun Academy built OmniEdu, an open family of K–12 learning-and-teaching foundation models, trained with a capability-oriented instruction corpus organized around subject competence, curriculum grounding, diagnostic reasoning, and pedagogical action and scaffolding (69,999 examples and 15.96M supervised response tokens, of which 60,951 examples and about 12.0M tokens are education-specific), fine-tuning 4B, 9B, and 27B backbones with full-parameter supervised fine-tuning and reporting consistent gains at every scale on curriculum-grounding, K–12 problem-solving, and pedagogical-tutoring benchmarks, with OmniEdu-27B reaching 63.12% EM / 76.69% F1 on K12-Bench, 85.89% on MathFish, 86.
Researchers from Peking University, the University of the Chinese Academy of Sciences, and Zhongguancun Academy built OmniEdu, an open family of K–12 learning-and-teaching foundation models, trained with a capability-oriented instruction corpus organized around subject competence, curriculum grounding, diagnostic reasoning, and pedagogical action and scaffolding (69,999 examples and 15.96M supervised response tokens, of which 60,951 examples and about 12.0M tokens are education-specific), fine-tuning 4B, 9B, and 27B backbones with full-parameter supervised fine-tuning and reporting consistent gains at every scale on curriculum-grounding, K–12 problem-solving, and pedagogical-tutoring benchmarks, with OmniEdu-27B reaching 63.12% EM / 76.69% F1 on K12-Bench, 85.89% on MathFish, 86.
OmniEdu fine-tunes 4B/9B/27B models on 69,999 capability-oriented examples, beating each base model across curriculum grounding, K–12 problem solving, and pedagogical tutoring
Researchers from Peking University, the University of the Chinese Academy of Sciences, and Zhongguancun Academy built OmniEdu, an open family of K–12 learning-and-teaching foundation models, trained with a capability-oriented instruction corpus organized around subject competence, curriculum grounding, diagnostic reasoning, and pedagogical action and scaffolding (69,999 examples and 15.96M supervised response tokens, of which 60,951 examples and about 12.0M tokens are education-specific), fine-tuning 4B, 9B, and 27B backbones with full-parameter supervised fine-tuning and reporting consistent gains at every scale on curriculum-grounding, K–12 problem-solving, and pedagogical-tutoring benchmarks, with OmniEdu-27B reaching 63.12% EM / 76.69% F1 on K12-Bench, 85.89% on MathFish, 86.
Researchers from Peking University, the University of the Chinese Academy of Sciences, and Zhongguancun Academy built OmniEdu, an open family of K–12 learning-and-teaching foundation models, trained with a capability-oriented instruction corpus organized around subject competence, curriculum grounding, diagnostic reasoning, and pedagogical action and scaffolding (69,999 examples and 15.96M supervised response tokens, of which 60,951 examples and about 12.0M tokens are education-specific), fine-tuning 4B, 9B, and 27B backbones with full-parameter supervised fine-tuning and reporting consistent gains at every scale on curriculum-grounding, K–12 problem-solving, and pedagogical-tutoring benchmarks, with OmniEdu-27B reaching 63.12% EM / 76.69% F1 on K12-Bench, 85.89% on MathFish, 86.
Nature Human Behaviour Sep 22, 2026
In the Tokyo Teen Cohort, 3,171 adolescents (46.9% girls) assessed at ages 12, 14 and 16 showed higher internalizing problems among girls at age 16 — depressive symptoms 15.9% versus 8.2% and suicidal ideation 29.8% versus 18.1% — and, after coproduction with young people identified loneliness and desire for thinness as candidate pathways, mediational g-formula estimates attributed 34.7% of the total effect of female sex on depressive symptoms and 46.3% of that on suicidal ideation to pathways through these factors at age 14, suggesting that social intermediates may contribute to the higher prevalence among girls in this cohort.
In the Tokyo Teen Cohort, 3,171 adolescents (46.9% girls) assessed at ages 12, 14 and 16 showed higher internalizing problems among girls at age 16 — depressive symptoms 15.9% versus 8.2% and suicidal ideation 29.8% versus 18.1% — and, after coproduction with young people identified loneliness and desire for thinness as candidate pathways, mediational g-formula estimates attributed 34.7% of the total effect of female sex on depressive symptoms and 46.3% of that on suicidal ideation to pathways through these factors at age 14, suggesting that social intermediates may contribute to the higher prevalence among girls in this cohort.
Loneliness and desire for thinness as pathways to girls' higher internalizing problems in the Tokyo Teen Cohort
In the Tokyo Teen Cohort, 3,171 adolescents (46.9% girls) assessed at ages 12, 14 and 16 showed higher internalizing problems among girls at age 16 — depressive symptoms 15.9% versus 8.2% and suicidal ideation 29.8% versus 18.1% — and, after coproduction with young people identified loneliness and desire for thinness as candidate pathways, mediational g-formula estimates attributed 34.7% of the total effect of female sex on depressive symptoms and 46.3% of that on suicidal ideation to pathways through these factors at age 14, suggesting that social intermediates may contribute to the higher prevalence among girls in this cohort.
In the Tokyo Teen Cohort, 3,171 adolescents (46.9% girls) assessed at ages 12, 14 and 16 showed higher internalizing problems among girls at age 16 — depressive symptoms 15.9% versus 8.2% and suicidal ideation 29.8% versus 18.1% — and, after coproduction with young people identified loneliness and desire for thinness as candidate pathways, mediational g-formula estimates attributed 34.7% of the total effect of female sex on depressive symptoms and 46.3% of that on suicidal ideation to pathways through these factors at age 14, suggesting that social intermediates may contribute to the higher prevalence among girls in this cohort.
arXiv Sep 22, 2026
Together, an external news story and two papers present two independent routes: one tests whether imaging lead rather than carbon in the ink can make text legible, using home-made burnt papyri, while the other uses synchrotron phase-contrast micro-CT plus machine learning to achieve the complete virtual unwrapping and reading of an intact, unopened Herculaneum roll, PHerc. 1667, recovers title and author evidence identifying PHerc. 139 as Philodemus, On Gods, Book 8, and provides volumetric ink validation in PHerc. Paris 4.
Together, an external news story and two papers present two independent routes: one tests whether imaging lead rather than carbon in the ink can make text legible, using home-made burnt papyri, while the other uses synchrotron phase-contrast micro-CT plus machine learning to achieve the complete virtual unwrapping and reading of an intact, unopened Herculaneum roll, PHerc. 1667, recovers title and author evidence identifying PHerc. 139 as Philodemus, On Gods, Book 8, and provides volumetric ink validation in PHerc. Paris 4.
Reading Without Unrolling: Virtual Unwrapping and Lead-Ink Imaging of Herculaneum Papyri
Together, an external news story and two papers present two independent routes: one tests whether imaging lead rather than carbon in the ink can make text legible, using home-made burnt papyri, while the other uses synchrotron phase-contrast micro-CT plus machine learning to achieve the complete virtual unwrapping and reading of an intact, unopened Herculaneum roll, PHerc. 1667, recovers title and author evidence identifying PHerc. 139 as Philodemus, On Gods, Book 8, and provides volumetric ink validation in PHerc. Paris 4.
Together, an external news story and two papers present two independent routes: one tests whether imaging lead rather than carbon in the ink can make text legible, using home-made burnt papyri, while the other uses synchrotron phase-contrast micro-CT plus machine learning to achieve the complete virtual unwrapping and reading of an intact, unopened Herculaneum roll, PHerc. 1667, recovers title and author evidence identifying PHerc. 139 as Philodemus, On Gods, Book 8, and provides volumetric ink validation in PHerc. Paris 4.
PLOS global public health Sep 21, 2026
This Opinion article proposes Workforce Science, arguing that amid population ageing, epidemiological transition, widening inequities and the rapid embedding of artificial intelligence, the health and care workforce should be reframed from a supply question of how many workers to educate and employ into a question of how hybrid human–AI capability, generated jointly by people, teams and intelligent technologies, is measured, analysed and stewarded; it offers a construct of effective capability (C*) as a function of education financing, education quality, labour, the human–AI dynamic, governance capacity and the conditions, rights and realities of work, together with a five-layer path of definition, measurement, analysis, governance and stewardship.
This Opinion article proposes Workforce Science, arguing that amid population ageing, epidemiological transition, widening inequities and the rapid embedding of artificial intelligence, the health and care workforce should be reframed from a supply question of how many workers to educate and employ into a question of how hybrid human–AI capability, generated jointly by people, teams and intelligent technologies, is measured, analysed and stewarded; it offers a construct of effective capability (C*) as a function of education financing, education quality, labour, the human–AI dynamic, governance capacity and the conditions, rights and realities of work, together with a five-layer path of definition, measurement, analysis, governance and stewardship.
Reframing the health workforce as a hybrid human–AI capability asset: the proposal of Workforce Science
This Opinion article proposes Workforce Science, arguing that amid population ageing, epidemiological transition, widening inequities and the rapid embedding of artificial intelligence, the health and care workforce should be reframed from a supply question of how many workers to educate and employ into a question of how hybrid human–AI capability, generated jointly by people, teams and intelligent technologies, is measured, analysed and stewarded; it offers a construct of effective capability (C*) as a function of education financing, education quality, labour, the human–AI dynamic, governance capacity and the conditions, rights and realities of work, together with a five-layer path of definition, measurement, analysis, governance and stewardship.
This Opinion article proposes Workforce Science, arguing that amid population ageing, epidemiological transition, widening inequities and the rapid embedding of artificial intelligence, the health and care workforce should be reframed from a supply question of how many workers to educate and employ into a question of how hybrid human–AI capability, generated jointly by people, teams and intelligent technologies, is measured, analysed and stewarded; it offers a construct of effective capability (C*) as a function of education financing, education quality, labour, the human–AI dynamic, governance capacity and the conditions, rights and realities of work, together with a five-layer path of definition, measurement, analysis, governance and stewardship.
medRxiv Sep 21, 2026
Using a sample of 20 U.S. osteopathic medical schools and eight educational vignettes yielding 160 school-vignette pairs, each assessed in three AI-assisted retrieval-and-evaluation runs and classified as Explicitly Supported, Inferable, Ambiguous, or Not Addressed, the study found that at least one eligible source was retrieved for 159 of 160 pairs (99.4%) yet only 21 pairs (13.1%) were grouped as sufficiently supported, most often for generative AI-assisted reflective writing (7/20 schools, 35%) and in no case for personal cloud notes or official clinical logs.
Using a sample of 20 U.S. osteopathic medical schools and eight educational vignettes yielding 160 school-vignette pairs, each assessed in three AI-assisted retrieval-and-evaluation runs and classified as Explicitly Supported, Inferable, Ambiguous, or Not Addressed, the study found that at least one eligible source was retrieved for 159 of 160 pairs (99.4%) yet only 21 pairs (13.1%) were grouped as sufficiently supported, most often for generative AI-assisted reflective writing (7/20 schools, 35%) and in no case for personal cloud notes or official clinical logs.
Decision Support in Publicly Available Patient Information Policies at U.S. Osteopathic Medical Schools: A Vignette-Based Document Analysis
Using a sample of 20 U.S. osteopathic medical schools and eight educational vignettes yielding 160 school-vignette pairs, each assessed in three AI-assisted retrieval-and-evaluation runs and classified as Explicitly Supported, Inferable, Ambiguous, or Not Addressed, the study found that at least one eligible source was retrieved for 159 of 160 pairs (99.4%) yet only 21 pairs (13.1%) were grouped as sufficiently supported, most often for generative AI-assisted reflective writing (7/20 schools, 35%) and in no case for personal cloud notes or official clinical logs.
Using a sample of 20 U.S. osteopathic medical schools and eight educational vignettes yielding 160 school-vignette pairs, each assessed in three AI-assisted retrieval-and-evaluation runs and classified as Explicitly Supported, Inferable, Ambiguous, or Not Addressed, the study found that at least one eligible source was retrieved for 159 of 160 pairs (99.4%) yet only 21 pairs (13.1%) were grouped as sufficiently supported, most often for generative AI-assisted reflective writing (7/20 schools, 35%) and in no case for personal cloud notes or official clinical logs.
arXiv Sep 21, 2026
Starting from Llama 3.1 8B, the study first fine-tunes a reviewer on official ICLR reviews from 2018–2023, then trains four successor models on ICLR 2024 data with synthetic reviews generated by that reviewer mixed at 0%, 33%, 66%, and 100%, finding that higher synthetic exposure compresses rating distributions and monotonically reduces same-paper and corpus-level semantic diversity (about 11% and 5%), a pattern the authors call scientific-judgment collapse, and introduces TrustReviewer, which mitigates this tendency through training-time corpus curation and test-time paired activation steering.
Starting from Llama 3.1 8B, the study first fine-tunes a reviewer on official ICLR reviews from 2018–2023, then trains four successor models on ICLR 2024 data with synthetic reviews generated by that reviewer mixed at 0%, 33%, 66%, and 100%, finding that higher synthetic exposure compresses rating distributions and monotonically reduces same-paper and corpus-level semantic diversity (about 11% and 5%), a pattern the authors call scientific-judgment collapse, and introduces TrustReviewer, which mitigates this tendency through training-time corpus curation and test-time paired activation steering.
When AI Reviews Train AI Reviewers: Scientific-Judgment Collapse and Mitigation
Starting from Llama 3.1 8B, the study first fine-tunes a reviewer on official ICLR reviews from 2018–2023, then trains four successor models on ICLR 2024 data with synthetic reviews generated by that reviewer mixed at 0%, 33%, 66%, and 100%, finding that higher synthetic exposure compresses rating distributions and monotonically reduces same-paper and corpus-level semantic diversity (about 11% and 5%), a pattern the authors call scientific-judgment collapse, and introduces TrustReviewer, which mitigates this tendency through training-time corpus curation and test-time paired activation steering.
Starting from Llama 3.1 8B, the study first fine-tunes a reviewer on official ICLR reviews from 2018–2023, then trains four successor models on ICLR 2024 data with synthetic reviews generated by that reviewer mixed at 0%, 33%, 66%, and 100%, finding that higher synthetic exposure compresses rating distributions and monotonically reduces same-paper and corpus-level semantic diversity (about 11% and 5%), a pattern the authors call scientific-judgment collapse, and introduces TrustReviewer, which mitigates this tendency through training-time corpus curation and test-time paired activation steering.
arXiv Sep 21, 2026
Without changing any parameters of the audio-language model, FRAUDSkill optimizes an external layer of skill programs, routing policies, and decision rules and combines structured output control with validation-guided multi-path inference, raising Macro-F1 on the TeleAntiFraud benchmark from 41.54% for the shared frozen-model baseline to 73.50% while cutting the invalid-output rate from 36.04% to 1.94%.
Without changing any parameters of the audio-language model, FRAUDSkill optimizes an external layer of skill programs, routing policies, and decision rules and combines structured output control with validation-guided multi-path inference, raising Macro-F1 on the TeleAntiFraud benchmark from 41.54% for the shared frozen-model baseline to 73.50% while cutting the invalid-output rate from 36.04% to 1.94%.
Structured Skill Optimization Under Frozen Weights: A New Path for Audio Anti-Fraud Detection
Without changing any parameters of the audio-language model, FRAUDSkill optimizes an external layer of skill programs, routing policies, and decision rules and combines structured output control with validation-guided multi-path inference, raising Macro-F1 on the TeleAntiFraud benchmark from 41.54% for the shared frozen-model baseline to 73.50% while cutting the invalid-output rate from 36.04% to 1.94%.
Without changing any parameters of the audio-language model, FRAUDSkill optimizes an external layer of skill programs, routing policies, and decision rules and combines structured output control with validation-guided multi-path inference, raising Macro-F1 on the TeleAntiFraud benchmark from 41.54% for the shared frozen-model baseline to 73.50% while cutting the invalid-output rate from 36.04% to 1.94%.

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