Rando, trek, promenade… des mots pour quel(s) sport(s) ? De la construction du discours d’autorité dans les blogs de tourisme d’aventure
Bibliographic record
Abstract
Dans cette contribution, j’analyserai les stratégies techno-discursives adoptées par des blogueurs-influenceurs de sports de marche de montagne (le trek, la promenade, la rando, le trail, etc.) dans leurs blogs en ligne, pour créer un discours d’autorité, digne de confiance. Après avoir cerné le phénomène des blogueurs-influenceurs dans le cadre du tourisme d’aventure, je montrerai que ce discours repose essentiellement sur la construction discursive d’un éthos numérique de crédibilité et sur des stratégies techno-discursives leur permettant d’augmenter leur visibilité et/ou de s’adresser tant à un public d’initiés qu’à de simples passionnés, voire curieux, souhaitant s’investir pour la première fois dans la pratique de sports d’aventure.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".