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Record W4390718764 · doi:10.1017/9781108779968

The Science of Virtue

2024· book· en· W4390718764 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCambridge University Press eBooks · 2024
Typebook
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVirtueEpistemologyCharacter (mathematics)Foundation (evidence)Value (mathematics)Moral characterEngineering ethicsResource (disambiguation)PsychologySociologyComputer sciencePhilosophyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Integrating psychological and philosophical research on virtue and moral development, this book presents a real-world program for virtue science. Offering empirically testable hypotheses, the chapters deliver theoretical and methodological guidance that shows how existing research can become a cohesive and truly interdisciplinary science of virtue. The authors' unique 'STRIVE-4 Model' defines a unifying conceptual framework, making the book an indispensable resource for a new generation of scholars and students. This empirically tested model provides the much-needed foundation that can put to rest traditional worries about moral science. While mapping out the relevant areas of psychology and value-focused inquiry, the book lays out an interdisciplinary approach to many questions, including the problem of knowledge about character. Written for those researching virtue drawing on personality, developmental, moral, and positive psychology, as well as moral philosophy and character education, the book demonstrates the importance and applications of studying virtues empirically.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.708
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.298
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it