L’habitant, le maître et le blanc-bec. Quelques souvenirs de « Monsieur Séguin »
Bibliographic record
Abstract
Pendant une douzaine d’années, j’ai eu le privilège de côtoyer Monsieur Séguin, tout d’abord en tant qu’élève de son cours sur L’équipement de la ferme canadienne aux xviie et xviiie siècles, qu’il donnait en 1966 aux Archives de folklore de l’Université Laval, puis en tant que collègue trois ans plus tard au sein de l’Institut national de la civilisation, sorte d’embryon d’un musée de l’homme mis sur pied par le ministre des Affaires culturelles de l’époque, Jean-Noël Tremblay. J’ai eu l’occasion à plusieurs reprises de le suivre sur le terrain, dans Charlevoix et en Mauricie et, par la suite, chez moi dans Kamouraska si bien que j’ai pu vivre en toute amitié ce rapport prolongé de maître à élève, de mentor hautement bienveillant et empreint de simplicité à l’égard du blanc-bec que j’étais.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".