Bibliographic record
Abstract
The present diploma thesis called In search of an equivocal country is devoted to the life and work of Victor-Lévy Beaulieu, a French Canadian (Quebecois) novelist. Beaulieu is an exceptional figure on the contemporary literary scene, especially because of his large scale project that he has been patiently pursuing for almost fifty years - the quest for a Quebecois epic and a hero that could save the nation from its loss of cultural identity. It is the 1978 trilogy Monsieur Melville that stands at the heart of the thesis. The eight hundred pages mosaic divided in three volumes might be mistaken for a simple biography of Herman Melville or for a critical analysis of his work. Nevertheless, it's to the art of literature that the text is devoted. Abel, writer and Beaulieu's alter-ego, is the narrator, while Melville assumes the role of an acolyte. Together, they undertake a voyage across New England and neighbouring waters in order for Abel to find strength enough to write the ultimate Book for Quebec. The thesis is made up by three main parts. Following a short introductory chapter concerned with Beaulieu's life and work and with his relation to the American literature, the focus is put on formal aspects of the trilogy. The first part of the thesis is called Monsieur Melville and postmodern inspiration and...
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".