Bibliographic record
Abstract
Je n’aime pas écrire, encore moins quand il faut suivre les codes sémantiques, stylistiques et institutionnels d’un doctorat de recherche-création en communication. Je n’aime pas écrire et je pourrais dire exactement la même chose au sujet de cet article : l’ensemble des codes du processus de rédaction d’un article scientifique me rebute. La différence ici est que, plutôt que d’en contourner les contraintes, je choisis d’y effectuer la mise en abîme de la compréhension du « pourquoi » je n’aime pas écrire. Si j’effectue ce mouvement vers moi pour mieux « me » comprendre, c’est bien entendu dans l’espoir de me faire comprendre par l’autre. Je n’aime pas écrire, parce qu’écrire, c’est communiquer à l’autre à l’aide de mots. Et la compréhension des codes pour y arriver représente pour moi une surcharge de travail et d’énergie. Pourquoi ? Parce que je suis autiste.
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".