La netnographie : mise en application d’une méthode d’investigation des communautés virtuelles représentant un intérêt pour l’étude des sujets sensibles
Bibliographic record
Abstract
La netnographie est apparue depuis quelques années comme la méthode de prédilection pour l’analyse des communautés virtuelles. Cette méthode privilégiée initialement dans les travaux portant sur le comportement du consommateur s’est ensuite élargie aux autres domaines des sciences sociales. Constituée de quatre étapes principales, elle s’inspire de la méthode de recherche ethnographique. Néanmoins, elle est animée de controverse au sein de la communauté académique en ce qui concerne la posture non participante du chercheur. Dans cet article, nous présentons tout d’abord la méthode ainsi que sa mise en application. Ensuite, et à partir de notre expérience de son utilisation, nous apporterons des éléments supplémentaires pour appuyer l’adoption de la posture non participante dans le cas de l’investigation des sujets sensibles.
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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 | 0.065 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".