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Record W3040432544

Naturalising Whiteness: Cultural competency and the perpetuation of White supremacy

2020· article· en· W3040432544 on OpenAlexaffabout
Morris Beckford

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsYork University
Fundersnot available
KeywordsWhite supremacyCognitive reframingSociologyPopulismIndividualismEnvironmental ethicsPublic relationsPolitical scienceGender studiesPolitical economyLawRacismPoliticsSocial psychologyPsychology
DOInot available

Abstract

fetched live from OpenAlex

Non-profit organisations engage with us from birth to grave.  In Canada, there are over 170,000 of them. Non-profits account for more ‘value add’ than motor vehicle manufacturing, mining, oil and gas extraction combined. Yet, in an increasingly diverse society where right-wing populism has, arguably, seemed to re-assert, re-root and reposition itself to reframe systems with which we engage on a daily basis, we still insist on using an individualistic approach to working with people who have been and continue to be harmed by those systems. A central problem is cultural competency and the ways in which it supports the positioning of race. In the Canadian context, despite historical and contemporary evidence to the contrary, Whiteness is positioned as the ideal and the normal. The concept of cultural competency helps to position Whiteness as the ideal against which race in organisations must be measured by allowing organisations to focus only on individuals and their inability to engage effectively with organisations and organisational systems, not on the systems. My argument is that the concept of cultural competency aids in the perpetuation and naturalisation of Whiteness as normative, thereby aiding in the maintenance of systemic oppression.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.042
GPT teacher head0.306
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations4
Published2020
Admission routes2
Has abstractyes

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