Le travail forcé et le travail des enfants dans les chaînes d'approvisionnement canadiennes : rapport annuel 2023-2024
Bibliographic record
Abstract
Le projet de loi d'intérêt public du Sénat S-211, édictant la Loi sur la lutte contre le travail forcé et le travail des enfants dans les chaînes d'approvisionnement et modifiant le Tarif des douanes, a pris effet le 1er janvier 2024. Cette Loi exige que le ou la responsable de chaque institution gouvernementale dont les activités comprennent la production, l'achat ou la distribution de biens au Canada ou à l'étranger présente un rapport annuel au ministre de la Sécurité publique et de la Protection civile, au plus tard le 31 mai de chaque année. Le présent rapport porte sur les mesures prises par le Conseil national de recherches du Canada (CNRC) pour prévenir ou réduire le risque de recours au travail forcé ou au travail des enfants à toute étape de la production de biens achetés entre la date d'entrée en vigueur de la loi et la fin du dernier exercice financier (31 mars 2024).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Not applicable | high |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Other design | low |
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".