Non-publics et MTE : étudier les raisons de ne pas visiter des organismes culturels selon une démarche enracinée
Bibliographic record
Abstract
Lors d’une recherche menée selon les principes de la méthodologie de la théorisation enracinée (MTE), nous avons étudié les raisons de ne pas visiter six organismes culturels établis en Mauricie. Cet article aborde les enjeux liés au concept central de notre étude, soit celui des non-publics de la culture. Nous y discutons les principes de la MTE et les nombreuses décisions d’ordre méthodologique que nécessite son application. De cette façon, nous explicitons les procédures mobilisées afin de gérer nos connaissances préalables sur la problématique, de collecter des données pertinentes, d’évoluer dans le projet en suivant une trajectoire hélicoïdale et d’analyser les données vers une compréhension cohérente avec les vécus des participants.
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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 | 0.022 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".