Quand des coureurs à pied amateurs ôtent leur montre connectée : étude des expériences corporelles de l’auto-quantification à partir d’une méthodologie par retrait
Bibliographic record
Abstract
Les expériences vécues de l’auto-quantification s’avèrent d’autant plus délicates à documenter qu’interviennent des processus infra-conscients d’incorporation sous l’effet d’une interaction durable avec l’outil digital. Dans le contexte de la course à pied amateure, nous suggérons d’utiliser le retrait de la montre connectée comme une méthodologie sui generis d’investigation des modalités incarnées d’adoption du – et d’adaptation au – dispositif de self-tracking. Sont confrontées deux études menées indépendamment, mobilisant respectivement un retrait ponctuel et imposé par le chercheur d’une part et un détachement volontaire et durable d’autre part. L’enjeu consiste à mettre en lumière les expériences corporelles, émotionnelles, sensorielles et attentionnelles suscitées par ces deux formes de suppression afin de révéler, de manière indirecte, des modes d’utilisation de la montre devenus transparents pour l’acteur.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".