Naturalising Whiteness: Cultural competency and the perpetuation of White supremacy
Bibliographic record
Abstract
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 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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.102 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".