‘I wanted to be white’: understanding power asymmetries of whiteness and racialisation
Bibliographic record
Abstract
This article uses a self-reflective autoethnography to critique colonisation and whiteness as systems of marginalisation and racialisation. I examine concepts grounded in post-colonial and anti-racist theories, and I interweave these with my experiences in white spaces in Colombia, the USA and Canada as an educator and researcher. I provide personal examples as data to explain how colonisation and whiteness have paved the road to my professional ‘success’, and I also illuminate how these have taken me away from understanding my cultural and linguistic roots. Contrary to conventional wisdom that formal education is empowering for racialised peoples, this article asserts that critical education has been fundamental to challenge inequality and power asymmetries. Finally, in reflecting on the depths to which whiteness has been entrenched in all aspects of my life and other racialised peoples, I seek determination and liberation by calling into question the normative historical raciolinguistic ideologies of whiteness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".