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Record W4205727248 · doi:10.32920/ryerson.14662692.v1

‘Such a white thing to do’: A discourse analysis of CAMH’s Coping with COVID-19 Campaign

2021· preprint· en· W4205727248 on OpenAlexaff
McKaila Sullivan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsWhite supremacyRacismComplicityJunctureCritical discourse analysisColonialismPraxisSociologyOperationalizationCoping (psychology)Gender studiesPoliticsCriminologyPolitical scienceLawIdeologyPsychologyEpistemologyLinguistics

Abstract

fetched live from OpenAlex

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

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

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 armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.012
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0140.026
Scholarly communication0.0100.008
Open science0.0020.007
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.058
GPT teacher head0.446
Teacher spread0.388 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical · Methods

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

Citations0
Published2021
Admission routes1
Has abstractyes

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