Bibliographic record
Abstract
Pour divers travaux pédagogiques, j’ai recueilli des récits populaires auprès de conteurs de Pubnico-Ouest. Parmi les particularités locales, plusieurs témoins combinent l’anglais et le français naturellement, ce qui reflète le bilinguisme fonctionnel de la population. Je me suis donc efforcée de transcrire mot à mot les narrations des informateurs, en respectant et en restant fidèle à la langue des conteurs, car il fallait préserver à l’écrit la spécificité orale de la région sans perturber la compréhension. Toutefois, cette méthode, qui régularise la graphie tout en conservant le mot-à-mot de la transcription, rend laborieuse la lecture pour ceux que rebute cette combinaison inhabituelle de l’anglais et du français. Faut-il corriger, récrire ou traduire les récits oraux de mon corpus pour les rendre accessibles au grand public? Comment alors rester fidèle aux récits des conteurs et à la langue macaronique qui représente leur réalité?
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".