How ‘Anti-ing’ becomes Mastery: Moral Subjectivities Shaped through Anti-Oppressive Practice
Bibliographic record
Abstract
Anti-oppressive practice (AOP) has been popularly adopted in the undergraduate and graduate levels as a dominant framework for theorising about oppression, the self and working towards change. It is conceptualised as the socially just framework to practise from when engaging racialised and marginalised populations. Using a post-structural Foucauldian analysis, I intend to examine the discursive effects of AOP as occupying a position of mastery. Specifically, the active process of ‘anti-ing’ is a way of governing the self which becomes a form of currency when it is taken up as a dominant discourse. Looking at three tenets of AOP theory relating to identity, authenticity and resistance, I suggest that AOP can operate to re-inscribe a normalcy that relies on the construction of a moral subjectivity, effectively obscuring the types of work that are required to modify and regulate oneself when performing ‘anti-ing’.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.100 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".