Bibliographic record
Abstract
Une creation collective du Theâtre de la Vieille 17 Terre d'accueil est l'aboutissement d'un projet d'animation theâtrale du Theâtre de la Vieille 17. Cette piece, elaboree a partir des experiences d'une trentaine d'immigrants francophones, presente le quotidien de six nouveaux arrivants a differentes etapes de leur adaptation au Canada. Pour creer le texte, les auteures Esther Beauchemin et Michele Matteau ont recueilli les impressions et les preoccupations des participants, les encourageant a livrer librement leurs sentiments lors de seances d'improvisation. Terre d'accueil nous incite a prendre conscience des difficultes rencontrees par les nouveaux arrivants dans leur pays d'adoption. Oscillant entre la voix poetique et le ton realiste, entre le rire et les larmes, cette ?uvre traduit les deceptions comme les joies intenses que vivent ces gens venus a la recherche d'une vie meilleure. Elle ouvre un espace de communion, dans l'espoir de rendre notre monde plus humain et plus sensible aux problematiques quotidiennes de ceux que nous recevons. Avec la participation de: Francine Anne Mercier, Yanick Dutelly, Jean-Pierre Nzeyimana, Marianne Bichara, Rose Teclaire Albertine Ekosso, Huguette Jean-Francois, Georges Seraphin, Elise Berthiaume, Veronique Rene, Rose Guerline Rene, Katiana Rene, Louis Mbani Antagana, Michel Shamoko-Tugena et Evalt Lemours.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.009 |
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".