The Disability Creation Process Model: A Comprehensive Explanation of Disabling Situations as a Guide to Developing Policy and Service Programs
Bibliographic record
Abstract
Understanding disability remains a challenge. Although the international community has largely embraced the idea that disability is the product of social and environmental practices, society still lacks good conceptual frameworks. In an era when the rights of persons with disabilities have been enshrined in international and national laws, such frameworks have become a necessity. Within the province of Quebec, Canada, the Disability Creation Process (DCP) model has served such a role. Furthermore, recent efforts to enrich the model enhance the applicability of this powerful tool to a broader range of contexts. As a result, the DCP model could be used more widely than it is today. In this paper we provide the foundations of the approach encapsulated by the model and explain how its usage guides policy development and service delivery within the province of Quebec. We also highlight its forward-looking capacities.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| 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; 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".