The Disability Policy Lens (2nd edition)
Bibliographic record
Abstract
The Disability Policy Lens was developed to assist scholars, policy-makers & advocates to analyze policy affecting people with disabilities in Canada. The seven questions take readers through a process of considering the implications for people with disabilities of specific policies. The Lens is unique in that it is brief, and non-prescriptive. There are no correct or incorrect answers to these questions, but each has important implications that should be carefully considered when creating or amending policy. La fiche The Disability Policy Lens a été conçue dans le but d’aider les universitaires, les décideurs et les défenseurs des droits à analyser les politiques qui touchent les personnes handicapées au Canada. Les sept questions amènent les lecteurs à prendre en considération les conséquences de politiques précises pour les personnes handicapées. La fiche est unique en ce sens qu’elle est brève et non prescriptive. Il n’y a aucune bonne ou mauvaise réponse à ces questions, mais chacune d’entre elles a des conséquences importantes qu’il convient d’examiner soigneusement lors de l’élaboration ou de la modification de politiques.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".