Est-ce un royaume paisible? Image du pays à travers les contes choisis
Bibliographic record
Abstract
The purpose of this analysis is to discuss the panorama of stories in Quebec, having a strong cultural background and dialoguing with different traditions. The authors of the article first outline the past of the story, originally oral, but enriched in the nineteenth century by a written version thanks to many writers. Thereafter, they make an inventory of the most common characters and motifs of traditional storytelling. However, even if the story is undoubtedly an integral part of the literary and cultural heritage of Quebec as evidenced by a long tradition of storytelling, it is definitely not homogeneous as among Quebec stories one can find particularly those classified as historical, anecdotal and supernatural. On top of this, stories change considerably with transformations in the Quebec society in the first half of the twentieth century. Even today, stories and storytellers are a major element in the cultural landscape of Canada, whether in literature or in the mass media, including television and the internet. Throughout the last century the story had moments of glory and decline, with a real revival in the 1990s. In order to illustrate new trends in the story that overlap with the old tradition, the authors focus on the creation of Fred Pellerin, a young artist, author of several collections of written and oral stories, who uses patterns and characters of ancient stories by giving them quite modern accents.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 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".