Bibliographic record
Abstract
Le point de départ de cette réflexion est la note de bas de page 48 de l’essai publié par Carlo Ginzburg en 1979, « Traces. Racines d’un paradigme indiciaire », où il est question d’une « épistémologie de type divinatoire ». À partir de cette note, j’ai voulu interroger les enjeux d’une théorie des connaissances mineures (non systématiques, non réitérables, comme l’intuition, l’analogie, la conjecture, des techniques informelles de savoir) pour les sciences humaines. Cette recherche a été guidée par une conviction d’écrivaine, qui publie par ailleurs des récits et des poèmes documentaires à partir d’archives privées, trouvées, institutionnelles. On peut qualifier le montage qui s’opère alors de « lyrisme critique », un lyrisme exact, mais qui donne voix aux traces, donne de la voix avec et pour les traces, ne renonce pas à l’expérience subjective de l’archive. Ce lyrisme pense et pleure, questionne et s’émeut, argumente et imagine, il donne forme publique à ce qui est souvent l’oublié de la res publica.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".