De la transcription — Quelques considérations sur l’édition en orature1
Bibliographic record
Abstract
Selon M. Jousse, « “[l]a mise par écrit” n’est toujours qu’un aide-mémoire plus ou moins grossier et inexact ». Et il est vrai que l’intonation, le timbre de la voix, les gestes du conteur, son débit, son expression corporelle et faciale n’ont pu être sauvés. Tout ce qui rend un mot vivant, vibrant, supporte difficilement le passage de l’oral à l’écrit. J.-J. Rousseau en convenait déjà : « L’écriture, qui semble devoir fixer la langue, est précisément ce qui l’altère; elle n’en change pas les mots, mais le génie; elle substitue l’exactitude à l’expression. » Mais si l’on veut tout de même faire oeuvre éditoriale, il faut bien se résoudre à trier, à sacrifier ceci pour transmettre cela. Je m’en suis tenu scrupuleusement à la voix en épousant l’enregistrement que j’en ai fait. En conséquence, la syntaxe des conteurs y est respectée ainsi que leur lexique et leurs tournures de phrase.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.052 | 0.034 |
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".