Bibliographic record
Abstract
Depuis une dizaine d’années, des écrivaines et écrivains s’emparent du format vidéo et de la publication sur YouTube pour proposer de nouvelles formes d’écriture. Cette « LittéraTube », à la fois multimédia et « plateformisée », réinterroge à la fois la figure de l’auteur et les pratiques sociolittéraires instituées. En s’appuyant sur l’état actuel du corpus de LittéraTube (par nature évolutif), tout en assumant une démarche empirique propre à l’observation participante, cet article s’intéresse à la façon dont la plateforme YouTube, en tant qu’hébergeur de contenus et réseau social, incite l’écrivain à réélaborer son mode d’existence et de légitimation au prisme du collectif. Entre logiques de communauté et mythe d’horizontalité, il entend ainsi sonder l’imaginaire du numérique que les écrivaines et écrivains participent à façonner.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.070 | 0.023 |
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".