Conceptualizing Addiction as Disability in Discrimination Law: A Situated Comparison
Bibliographic record
Abstract
People labeled as having an addiction and people with disabilities face significant discrimination in their daily lives. In countries where targeted disability discrimination law is applied, it is often assumed that including addiction in the definition of disability will protect those labeled as having an addiction from discrimination. Several scholars have considered the effects of excluding addiction from the remit of discrimination law, but there has been less work examining the consequences—both positive and negative—of including addiction. Using the method of “situated comparisons” developed by intersectionality scholars, this article interrogates how addiction and disability are co-constituted in two contrasting legal and geographical contexts, where people labeled as having an addiction have sought to assert their right to equality before the law. By comparing the application of targeted discrimination law in Australia with a human rights charter in Canada, it demonstrates how systems of power such as ableism and neoliberalism work through the law to co-constitute addiction and disability in ways that are stigmatizing, even within legal approaches that aim to eliminate discrimination. Furthermore, the law, in both contexts, fails to recognize the intersectional nature of discrimination often experienced by these groups. The article contends that conceptualizing addiction as a disability will not necessarily reduce the discrimination faced by people labeled as having an addiction and concludes with recommendations for both policy and legal practice.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.114 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".