Bibliographic record
Abstract
Despite continuing investigations of the efficacy of Canadian addiction treatment services and supports across a range of health care settings and socio-cultural groups, many systemic, geographic and ideological barriers to service provision for women still exist. Determining how current services and supports can become more congruent with women's gender-specific needs is a current research focus. Drawing on Butler's reformulation of Althusser's interpellation, this article explores the power of hailing, where hailing power lies, and how hailing operates in discourses about addiction that appear in women's talk of their encounters with addiction services and supports. The article briefly outlines Butler's understanding of interpellation and examines ways by which gender operates as both condition and effect in women becoming addicts. I argue that women's narratives reveal patterns of interaction that intersect and generate complex social meanings and identities, and serve to get women's attention in terms of seeing themselves as addicts. Further, I argue that powerful competing discourses concerning gender and the medicalization of addiction, hailed through these interactions, are taken up as lived realities by some women and resisted by others. Knowing how women are hailed to take up as their own, or resist, aspects of traditional and gendered discourses within addiction treatment and recovery communities can inform gender-compassionate service provision.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.038 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".