Bibliographic record
Abstract
Régis Messac (1893–1945), French author of science fiction, satire and social criticism taught French at Montreal’s McGill University from 1924 to 1929. A provocative thinker whose works are now being republished, his years at McGill inspired harsh criticism of American society and a satirical novel Smith Conundrum about the trials of teaching at a thinly disguised McGill. His satire is college fiction in the tradition of his fellow professor Stephen Leacock but harsher. This article reconstructs Messac’s career at McGill, links his journalism during that time with his novel and traces how closely he used real people and events at McGill as inspirations for his satire. The article mainly draws on McGill’s archival records, calendars, and student publications as well as Messac’s own publications.ResuméRégis Messac (1893–1945), auteur français d’oeuvres de science fiction, de satire et de critique sociale, enseigna le français à l’Université McGill à Montréal de 1924 à 1929. Un penseur provocant dont les oeuvres sont aujourd’hui en réédition, ses années à McGill l’ont mené à critiquer sévèrement la société américaine et ont inspiré son roman Smith Conundrum qui traite des difficultés de l’enseignement à une Université McGill qui n’y est que très légèrement déguisée. Son oeuvre satirique de fiction collégiale est dans la tradition de son collègue, le professeur Stephen Leacock, mais est de nature plus sévère. Cet article reconstruit la carrière de Messac à McGill, établit des liens entre son journalisme et son roman durant cette époque, et examine la manière dont des gens et des événements à McGill lui ont servi de source d’inspiration directe pour son oeuvre satirique. L’article utilise principalement des documents d’archives, des calendriers et des publications étudiantes à McGill ainsi que les oeuvres publiées par Massac lui-même.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".