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Record W2555090082 · doi:10.4000/books.pum.4305

Facteur d’impact

2015· book-chapter· fr· W2555090082 on OpenAlexfundno aff
Vincent Larivière

Bibliographic record

VenuePresses de l’Université de Montréal eBooks · 2015
Typebook-chapter
Languagefr
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignOrganisation de Coopération et de Développement ÉconomiquesUniversity of TorontoJohns Hopkins UniversityPrinceton UniversityUniversity of OxfordHarvard UniversityUniversity of CambridgeWayne State UniversityState University of New YorkStanford Bio-XStyrelsen för Internationellt Utvecklingssamarbete
KeywordsMaterials science

Abstract

fetched live from OpenAlex

Créé dans les années 1960 par Eugene Garfield, l’un des fondateurs de la scientométrie (voir Bibliométrie), le facteur d’impact est un indicateur qui, depuis une dizaine d’années, fait couler beaucoup d’encre dans la communauté scientifique : une mesure de l’« impact » scientifique des revues. À l’origine, cet indicateur avait été mis au point pour aider les bibliothécaires à choisir leurs abonnements à des périodiques scientifiques. Graduellement, il est cependant dev

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.011
Science and technology studies0.0030.004
Scholarly communication0.0130.008
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0430.009

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 head0.281
GPT teacher head0.411
Teacher spread0.130 · 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
GenreMethods

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

Citations0
Published2015
Admission routes1
Has abstractyes

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