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Record W3087444756 · doi:10.1051/nss/2022008

La prédation dans le champ de la publication scientifique : un objet de recherche révélateur des mutations de la communication scientifique ouverte

2021· article· fr· W3087444756 on OpenAlexfundno aff
Chérifa Boukacem‐Zeghmouri, Sarah Rakotoary, Pascal Bador

Bibliographic record

VenueNatures Sciences Sociétés · 2021
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
FundersUniversité du Québec à Montréal
KeywordsHumanitiesPhilosophyPolitical science

Abstract

fetched live from OpenAlex

L’article présente un état de l’art critique du phénomène des revues prédatrices qui touche le champ de la communication scientifique et en fait une de ses actualités les plus médiatisées. Il rend compte des débats en cours, des recherches menées et de leurs méthodologies. L’article discute la définition de la revue prédatrice et propose une analyse du nouveau marché de listes de revues « légitimes » et « illégitimes ». Il identifie les principaux enjeux éthiques et scientifiques que les revues prédatrices font peser sur la publication en Libre Accès et rend compte des contextes qui conduisent des chercheurs (jeunes et seniors) à y publier. En rattachant les revues prédatrices au champ de la communication scientifique, l’article en pointe les principales problématiques et les érige en objet de recherche. L’article conclut sur des pistes de recherches contribuant à l’analyse des mutations de la communication scientifique numérique.

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)0.039
metaresearch head score (Gemma)0.100
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Open science, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.100
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.007
Science and technology studies0.0120.053
Scholarly communication0.0310.031
Open science0.0030.011
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0110.002

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 head0.411
GPT teacher head0.457
Teacher spread0.046 · 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 designQualitative
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

Citations6
Published2021
Admission routes1
Has abstractyes

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