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Record W4398249663 · doi:10.22329/csw.v25i1.8266

In bed with the state

2024· article· en· W4398249663 on OpenAlexaffvenueabout
R. P. Pereira

Bibliographic record

VenueCritical Social Work · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsState (computer science)Environmental scienceGeologyComputer scienceProgramming language

Abstract

fetched live from OpenAlex

The aim of this paper is to unsettle dominant discourses about the state and forensic social work though “dehistoricization” through two historical case studies from Canada: the eugenics movement and the Indian Act. This critical and structural analysis will demonstrate how the state historically and contemporarily disproportionately disadvantages disabled and Indigenous peoples independently and intersectionally through social control tactics. Using a conceptual framework from Foucault’s History of the Present approach, I problematize how historical practices manifest in present-day technologies of the state. I also utilized Blackstock’s Touchstones of Hope, a model for reconciliation in children’s services through four phases: Truth Telling, Acknowledging, Restoring, and Relating. After situating myself in the work, I will then articulate the key concepts including forensic social work, the state, intersectionality, and settler colonialism followed by the dominant historical discourses in forensic social work. Next, I dehistoricize the eugenics movement and the Indian Act as case studies to demonstrate how the state used and uses social control tactics of surveillance, categorization, segregation, and containment of disabled and Indigenous peoples. Contemporary forensic social work practices with disabled and Indigenous peoples will demonstrate that the historical practices continue in the present. I conclude with a call for reconciliation and transformative change.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.349
Teacher spread0.333 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2024
Admission routes3
Has abstractyes

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