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.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".