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Record W4248627056 · doi:10.32316/hse/rhe.v26i1.4382

End matter

2014· article· fr· W4248627056 on OpenAlexafffundvenue
Daniel G. Ross

Bibliographic record

VenueHistorical Studies in Education / Revue d histoire de l éducation · 2014
Typearticle
Languagefr
Field
Topic
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsComputer science

Abstract

fetched live from OpenAlex

Historien de formation, Jean-François Cardin est didacticien de l'histoire et des sciences sociales à l'Université Laval.Ses principaux intérêts de recherche portent sur les questions d'identités nationales en enseignement de l'histoire, sur l'apprentissage des concepts, sur l'histoire des programmes et de l'enseignement de l'histoire et sur la formation des enseignants.Il a conçu du matériel pédagogique pour les écoles et s'intéresse aussi au manuel scolaire en histoire.Kurt Clausen is associate professor at Nipissing University, North Bay.He is also the editor-in-chief of the Canadian Journal of Action Research.His research interests centre on innovative educational past practices and thought.His most recent SSHRC-funded research examines the creation and legacy of the Hall-Dennis Report.He has recently published in this area in History of Education, Teacher and Teacher Education, and Religion & Education.Sol Cohen is professor in the Graduate School of Education, UCLA.He teaches courses in the history of American education, historiography, philosophy of history, and a course on Hollywood views of American teens and secondary education.He is the author of Challenging Orthodoxies (1999); several essays dealing with the oeuvre of historian Lawrence Cremin in the pages of HSE/RHÉ; and more recently, of "Memoir: A Mosaic of Memories" in Wayne J. Urban, ed.Leaders in the Historical Study of American Education (2011).

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.244
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0090.002
Open science0.0020.003
Research integrity0.0110.004
Insufficient payload (model declined to judge)0.7560.728

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 head0.085
GPT teacher head0.339
Teacher spread0.254 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2014
Admission routes3
Has abstractno

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