El TNO y la evolución de la comunidad\n\t\t\t\t francófona de Sudbury (Ontario)
Bibliographic record
Abstract
In 1971 “Le Théâtre du Nouvel Ontario” was\n\t\t\t\t founded in Sudbury (in the Northeast of Ontario)\n\t\t\t\t by a group of university students, willing\n\t\t\t\t to answer the questions “Who are we?” by the\n\t\t\t\t means of the arts and, specially, theatre. Those\n\t\t\t\t pieces of work were based on the everyday life\n\t\t\t\t (they talked about traditions and discrimination,\n\t\t\t\t but also about the possibility of a change for\n\t\t\t\t the better), were addressed to the young and\n\t\t\t\t the working class, and were written in the language\n\t\t\t\t of the region. Later, the authors as well\n\t\t\t\t as the public preferred more universal topics,\n\t\t\t\t and going to the theatre became an artistic experience.\n\t\t\t\t Nowadays, according to sociologists\n\t\t\t\t Laflamme and Mainville (2007), in order to get\n\t\t\t\t to know the people who go to the theatre, it is\n\t\t\t\t necessary to ask them about their likes, opinions\n\t\t\t\t and feelings. This methodology has been\n\t\t\t\t applied as the frame of work for the analysis of\n\t\t\t\t some data collected through field work among\n\t\t\t\t the French-speaking community of Sudbury in\n\t\t\t\t 2006.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 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".