Un corpus nommé désir : le laboratoire d’étonnement pour réintroduire l’affect dans la recherche
Bibliographic record
Abstract
Cette contribution présente les fondements d’un laboratoire d’étonnement comme moyen méthodologique de transformer un objet de déception en objet de désir pour le chercheur. Tout d’abord, les auteurs proposent une lecture spinoziste de leurs émotions causées par leur confrontation avec un matériau étranger au domaine de la psychologie, un docu-feuilleton sur le décrochage scolaire intitulé « Les persévérants » (Ferron et Baer, 2014). Ils considèrent l’étonnement comme un affect pour renouveler l’intérêt du chercheur pour son sujet de recherche. Ils en examinent ensuite les conditions d’émergence. Enfin, les conditions de mise en place d’un laboratoire d’étonnement sont illustrées par des exemples tirés de la littérature et des pratiques des auteurs.
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How this classification was reachedexpand
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".