<i>Interposture : des postures multiples </i> <i>au bénéfice de la recherche</i>
Bibliographic record
Abstract
Cet article vise à apprécier comment l’interposture contribue à la richesse des données et des analyses dans le cadre d’une recherche qualitative. En effet, dans la réalisation d’une étude de cas multiples inspirée de la recherche collaborative, la chercheuse mobilisait d’emblée cinq postures indissociables d’elle-même et du contexte de recherche : chercheuse principale, experte en évaluation, chargée de cours, conseillère pédagogique et technopédagogue. De ce fait, elle a utilisé chacune des postures de manière à optimiser son contact avec les personnes participantes, s’inscrivant dans une épistémologie participative dans laquelle ces différentes postures contribuent à une production de données riches et approfondies. Cet article fait la lumière sur l’approche adoptée par la chercheuse, sur ses multiples postures inhérentes, ainsi que sur l’apport de la méthodologie retenue pour la recherche sur le terrain et spécifiquement sur les pratiques évaluatives.
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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 | 0.077 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
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".