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Record W2907994858 · doi:10.1177/0091450918819519

Conceptualizing Addiction as Disability in Discrimination Law: A Situated Comparison

2018· article· en· W2907994858 on OpenAlexaboutno aff
Rebecca Bunn

Bibliographic record

VenueContemporary Drug Problems · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicFeminism, Gender, and Sexuality Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAddictionSituatedSociologyPsychologyLawPolitical scienceCriminologyPsychiatry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0100.114
Scholarly communication0.0120.016
Open science0.0020.016
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.124
GPT teacher head0.385
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2018
Admission routes1
Has abstractyes

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