Intégrer les points de vue des élèves dans les recherches en contexte scolaire : promesses théoriques et écueils pratiques de l’ethnographie visuelle
Bibliographic record
Abstract
L’ethnographie visuelle implique d’integrer la camera video a la pratique d’enquete de terrain. Dans cet article, l’ethnographie visuelle s’entend comme une methodologie de recherche dont la proposition theorique sous-jacente est de produire des films avec les participants, plutot que sur eux. Les deux elements centraux qui constituent le coeur de la proposition — a savoir, un partage de la responsabilite de filmer et un partage de la responsabilite de ce qui sera represente dans le film ethnographique — sont explicites puis illustres, et ce, dans l’optique de mieux integrer les points de vue des participants. Abstract Visual ethnography implies integrating the video camera into fieldwork practices. In this article, visual ethnography is understood as a research methodology, the underlying theoretical proposition of which is to produce films with participants rather than produce films about them. The two central elements of this proposition, namely, a shared responsibility for filming and a shared responsibility for what is to be represented in the ethnographic film are explained and then illustrated, in order to focus on better integrating the participants’ points of view.
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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 | 0.047 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.014 | 0.052 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".