Bibliographic record
Abstract
Peut-on etudier un opera a l'ecole primaire C'est a cette question que je tente de repondre dans mon memoire. Il est interessant de se la poser parce que derriere cette thematique de l'enseignement de l'opera a l'ecole elementaire se cache un riche questionnement quant a sa mise en oeuvre. Qu'est ce que l'opera ? Qu'est-ce qu'enseigner l'opera a l'ecole primaire ? Pourquoi et comment l'enseigner ? Ce sont a toutes ces questions que je tente de repondre ici. Afin d'elucider le probleme pose par le sujet, j'ai mene une experience dans une classe d'ecole primaire en Ontario au Canada. L'opera 'Carmen' de Georges Bizet a ete etudie. Vous decouvrirez dans mon memoire la mise en oeuvre de l'etude de cet opera, son analyse, mes reussites, ainsi que mes difficultes. Decouvrir ces elements est important car ce sont eux qui permettent de degager toutes les caracteristiques propres a l'enseignement de l'opera.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".