La méthodologie de la théorisation enracinée et les grandes enquêtes publiques
Bibliographic record
Abstract
Les grandes enquêtes publiques portent sur le parcours professionnel du communicateur social. Rarement toutefois elles furent abordées selon une approche inductive. Deux grandes investigations sont ici prises à témoin. La première est une expertise collégiale menée dans le cadre de la lutte contre la propagation de la dengue. L’autre traite de la tournée du Conseil de presse qui visait à faire le bilan de l’information au Québec. Liées théoriquement de manière surprenante, les deux grandes enquêtes sont revues dans la perspective de la méthodologie de la théorisation enracinée (MTE). Séquencées de façon opérationnelle et examinées en détail, on y constate que la volonté, dans les deux exercices, de se rapprocher des préoccupations citoyennes place ces démarches dans une grande parenté avec la MTE. La force de l’approche inductive y transparaît de manière non équivoque et la démonstration de la pertinence de son usage lors de pareilles enquêtes devient évidente.
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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 | 0.039 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".