Bibliographic record
Abstract
Faites de beaux rêves de Jacques Poulin raconte les aventures de Limoilou, d’Amadou et de son frère Théo lors d’une joyeuse escapade au Mont-Tremblant où ils assistent au Grand Prix du Canada de Formule 1. Entre les courses des « dix mille chevaux lâchés d’un coup » sur la piste, ce trio de gais lurons passera son temps « à inventer toutes sortes d’histoires ». La mise en récit du réel et de l’imaginaire sera pour eux l’occasion d’une véritable « métamorphose ludique de l’ego » (Ricoeur). Incarnant une sorte de Don Quichotte québécois aux allures de mousquetaire chevaleresque et de cowboy téméraire, Théo s’avèrera être à la fois un modèle et un guide pour Amadou, « commis aux écritures » en quête de son identité. Cette étrange petite fable poulinienne est en quelque sorte une invitation amicale à se laisser prendre au jeu de la littérature, autrement dit, à apprendre à rêver le monde autrement.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 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".