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Record W4212802579 · doi:10.1051/medsci/2021261

Éléments d’intégrité scientifique à considérer pour limiter les manquements dans la recherche en psychologie

2022· article· fr· W4212802579 on OpenAlexaff
Oriane Simion

Bibliographic record

Venuemédecine/sciences · 2022
Typearticle
Languagefr
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophySociology

Abstract

fetched live from OpenAlex

La notion de consentement interpelle l’opinion publique, le corps médical, les tribunaux, et les chercheurs tentent d’en discerner les contours, le domaine de la recherche n’en étant pas exclu. En psychologie, les études explorent le fonctionnement de l’être humain et ne peuvent être effectuées sans le consentement du participant. Qu’elle soit conduite sur la population générale ou sur des personnes atteintes de troubles spécifiques, la recherche en psychologie doit intégrer la participation de personnes qui sont explicitement consentantes. Nous explorerons, dans cette revue, les spécificités et les obstacles du recrutement impliquant un consentement libre et éclairé en psychologie. Nous aborderons ensuite les éléments d’intégrité scientifique à considérer, afin de limiter les possibles inconduites scientifiques.

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)0.085
metaresearch head score (Gemma)0.054
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0850.054
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.007
Scholarly communication0.0000.000
Open science0.0030.002
Research integrity0.0010.008
Insufficient payload (model declined to judge)0.0100.001

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 head0.890
GPT teacher head0.652
Teacher spread0.238 · 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 designTheoretical or conceptual
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

Citations0
Published2022
Admission routes1
Has abstractyes

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