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The Canada Research Chairs Program and Social Science Reward Structures

2008· article· fr· W2013594586 on OpenAlexaffabout
Kyle Siler, Neil McLaughlin

Bibliographic record

VenueCanadian Review of Sociology/Revue canadienne de sociologie · 2008
Typearticle
Languagefr
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPolitical scienceHumanitiesSociologyPhilosophy

Abstract

fetched live from OpenAlex

Les auteurs analysent les comptes rendus des publications et des citations des Chaires de recherche du Canada en sociologie, en science politique et en économie au cours des cinq années pendant lesquelles le programme s'est poursuivi. Ils les comparent à des échantillons aléatoires de comptes rendus de publications et de citations qui ne sont pas le fruit des Chaires de recherche du Canada dans leur discipline respective pour tester leur qualité professionnelle. Les données et les analyses démontrent que les membres de ces Chaires de recherche constituent une population hétérogène ayant peu de «vedettes» authentiques et dont plusieurs personnes présentent des comptes rendus de publications et de citations semblables ou inférieurs à ceux de leurs collègues qui ne sont pas membres des Chaires de recherche du Canada. Les auteurs explorent la monotonie institutionnelle, l'appropriation institutionnelle ainsi que la périphéricité et l'organisation disciplinaire canadiennes en tant qu'explications possibles de ces résultats. This article analyzes the publishing and citation records of Canada Research Chairs (CRCs) in sociology, political science, and economics over the first 5 years of the program. Publication and citation records of CRCs are compared with random samples of non‐CRCs in their respective disciplines as empirical tests of professional strength. The data and analyses suggest that CRCs are a heterogeneous population with a few obvious “stars” and many with publishing and citation records similar or inferior to their non‐CRC peers. Institutional flatness, institutional appropriation, and Canadian peripherality and disciplinary organization are explored as possible explanations for these results.

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 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)0.064
metaresearch head score (Gemma)0.085
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Bibliometrics, Science and technology studies, Open science, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0640.085
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.040
Science and technology studies0.0080.040
Scholarly communication0.0000.000
Open science0.0060.001
Research integrity0.0010.002
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 head0.584
GPT teacher head0.538
Teacher spread0.047 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2008
Admission routes2
Has abstractyes

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