<i>Poussez ! Coupez !</i> Une recherche-création pour repenser l’accouchement dans la fiction
Bibliographic record
Abstract
Dans cet article, j’expose ma démarche de recherche-création en média expérimental : une exploration esthétique et théorique des représentations audiovisuelles de l’accouchement, à travers la méthode du ciné-collage. Forme, et pratique, basée sur l’amalgame de fragments hétérogènes, le ciné-collage permet d’exacerber les tensions, tout en favorisant de nouvelles représentations de l’accouchement. Revenant sur des créations déjà accomplies, en continuité avec des méthodes de la recherche-création qui prévoient d’étudier la rétroaction, je m’attarde sur des questions épistémologiques, parfois contradictoires, de diverses approches féministes de la maternité. Cela me permet de mettre à jour mes propres biais culturels, tout en repensant mon propre rapport aux images et à la narration.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".