Knowledge Translation from Disability Studies to Policy Makers: Literature Review and Expert Consultation
Bibliographic record
Abstract
Disability policy is currently receiving more attention than it has in the past 35 years. How have disability studies researchers participated in these processes, providing the results of their research to ensure the best possible evidence-based policy? This paper reviews the literature on barriers to knowledge translation from disability studies researchers to policy-makers, as well as the incorporating the recommendations of a high-level expert panel of experienced policy makers in disability portfolios. The principal barriers identified are: awareness of the policy process, awareness of government’s agenda, timing of information, format of the message, and commitment to the relationship. The panel offers five recommendations to address these barriers.
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 | 0.347 | 0.566 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.037 | 0.028 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".