Pratiques informationnelles des youtubeurs scientifiques au service de la médiation du savoir
Bibliographic record
Abstract
Depuis 2005, l’information de sujets scientifiques et culturels à des publics non spécialisés se transmet par vidéos sur Youtube, favorisant ainsi l'accès aux discours de vulgarisation. Ces vidéastes scientifiques s’enrichissent d’une spécialisation et jouent un rôle important dans la vulgarisation des savoirs et / ou leur médiation. Afin de mieux appréhender les relations entre sciences, recherche et société, l’auteure examine le point de vue de ces youtubeurs que l’on qualifie de pro-am, c’est-à-dire un amateur-professionnel, sur leur activité et les compétences informationnelles qu’ils mobilisent. Ce succès des youtubeurs pro-am s’expliquerait par l’éthos de l’expert qui ne semble plus reposer uniquement sur des critères académiques comme les diplômes ou la profession
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".