‘Such a white thing to do’: A discourse analysis of CAMH’s Coping with COVID-19 Campaign
Bibliographic record
Abstract
This major research paper is a modified critical discourse analysis of lived experience testimonials from the Centre for Addiction and Mental Health (CAMH)’s Coping with COVID-19 campaign. Social work practitioners and researchers must consider the inherent violence in the complex manifestations of sanism and racism (re)produced through discourse and their inextricable confluence with institutions, colonial legacies and realities which operate at this juncture in support of white supremacy. The identified discourses reproduce the ideal neoliberal subject and operate as technologies which maintain the colonial project and white supremacy. If we stake any claim to anti-racist praxis at this juncture, it is necessary to radically disclose our complicity within this colonial project, acknowledge our confluent realities and interrogate any claim to anti-racism. If we fail to interrogate these discourses constructing madness, we not only permit the violent trajectory of sanism but operationalize the deeply entrenched (re)production of violent white supremacy. key words: critical discourse analysis, sanism, racism, white supremacy, psychocentrism
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".