Bibliographic record
Abstract
La crise de réplication scientifique que le domaine de la psychologie traverse en ce moment remet en question la réputation de notre discipline et notre confiance dans une majorité de recherches passées et présentes. Le présent article couvre les points les plus importants en lien à la crise de réplication et la science ouverte, via une brève revue de littérature. Deux causes principales de la crise de réplication en psychologie ressortent : les pratiques de recherche douteuses et le manque de transparence. Heureusement, la science ouverte, qui met au cœur de sa démarche la transparence, la reproductibilité et les bonnes pratiques de recherche, permet d’adresser ces deux problématiques directement. Celle-ci recommande notamment : (a) le préenregistrement de l’étude; (b) le rapport enregistré; (c) la mise en ligne publique des données désidentifiées; (d) la mise en ligne des matériels et de la syntaxe; (e) l’utilisation de logiciels libres tels que R; (f) la prépublication; et (g) la publication en libre accès. Cet article couvre brièvement ces différentes pratiques.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Open science Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.615 | 0.818 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.030 | 0.040 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".