Bibliographic record
Abstract
Evidemment ces congres etaient Foccasion de se trouver en presence de grands ecrivains du monde francophone: par exemple, Antonine Maillet (Moncton), Mongo Beti (Tunisie), Marie-Claire Blais (Gatineau), Jeanne Castille (Lafayette), Anne Hebert (Montreal), Calixthe Beyala (Quebec), pour n en mentionner que quelques-uns. Et bien entendu cest a ces congres que jai enormement appris, en ecou tant les communications et en participant moi-meme a des sessions. Je me souviens en particulier de cette session a la Nouvelle Orleans avec trois grandes dames des etudes francophones: Mary Jean Green, Jane Moss et Karen Gould. Et je nbublierai jamais Lucille Martineau a Lafayette (aussi a Moncton). Elle ne lisait jamais ses communications fascinantes. Malheu reusement, Lucille nest plus. Je me souviens aussi dune session plus recente a Gatineau, ou il sagissait de parler de film. Cest la que jai retrouve Elisa beth Locey (que je connais depuis son enfance) et que jai lie amitie avec la cineaste tuniso-montrealaise Hejer Charf. Grace aux participants des congres jai appris enormement de choses. Je pense aux contributions de professeurs tels que Renee Linkhorn, Frans Amelinckz, Bernard Aresu, Raymonde Bulger, Jack Yaeger, Emile Talbot, parmi tant dautres.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.095 | 0.026 |
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".