Bibliographic record
Abstract
Cultural appropriation is a contentious topic, and disagreement persists about whether and why it is morally wrong.A common view takes the wrong of cultural appropriation to consist in causing offense to members of the appropriated culture. 1 Common as this view may be, criticisms of cultural appropriation often focus on different and arguably more serious concerns than offense.Take for example Loretta Todd, a Métis Cree scholar, filmmaker, and activist in Canada, who argues that cultural appropriation is the "inversion" of "cultural autonomy"; more specifically, Cultural autonomy signifies a right to one's origins and histories as told from within the culture and not as mediated from without.Appropriation occurs when someone else speaks for, tells, defines, describes, represents, uses or recruits the images, stories, experiences, or dreams of others for their own.Appropriation also occurs when someone else becomes the expert on your experience and is deemed more knowledgeable about who you are than yourself.2 Todd's remarks are representative of a criticism of cultural appropriation that ties its wrongness to a broader concern about cultural colonialism: roughly, the worry that the end of formal colonial rule failed to end in turn I want to thank Annie
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.124 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".