Bibliographic record
Abstract
Jean-Pierre Wallot was born in Salaberryde-valleyfield, Québec on 22 May 1935.His father was of Belgian descent and his mother, French-Canadian.He grew up in a family that placed a high value on education and intellectual debate.As a teenager he worked for the family newspaper to earn money while he attended school and university, and he apparently was a very good newspaper man as he won prizes in journalism in the Eastern Townships region in the late 1950s.Among the lessons he learned in the newspaper business were a strong work ethic, the ability to spend late hours over a keyboard, and how to meet the rigid demands of a publishing deadline.The benefits of that training were apparent in his later years as a researcher, professor, author, and administrator both in academia and in the public service.Dr. Wallot had a long association with the Université de Montréal (UM) where he received a BA in 1954, an MA in 1957, and a PhD in 1965.He became a tenured professor at the university in 1973, and served as an administrator in several capacities including vice-rector of studies, and vice-dean of research in the faculty of arts and sciences.In the 1980s he served as vicerector of academic affairs.Prior to his work at UM, he worked as a historian at the National Museum in Ottawa for three years, as an associate professor at the University of Toronto from 1969 to 1971, and as a visiting professor at the
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.156 | 0.021 |
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".