Évaluation du sous-dénombrement de la population lors du recencement de la population et du logement du Canada de 1976
Bibliographic record
Abstract
La contre-vérification des dossiers constitue l’une des principales études de la qualité des données produites lors du recensement de la population et du logement du Canada de 1976. Elle vise à analyser le sous-dénombrement de la population lors du recensement et ses effets sur les chiffres de la population pour le Canada, les provinces et certains sous-groupes importants. La méthode consiste à choisir un échantillon de personnes à partir de sources indépendantes du recensement de 1976, à établir l’adresse de chacune au moment du recensement et à vérifier dans les documents du recensement si elles ont ou non été recensées. Le présent document décrit d’une manière générale la méthodologie de la contre-vérification des dossiers de 1976 et donne les principaux résultats obtenus.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".