Bibliographic record
Abstract
Social media is full of accusations and counter-accusations of a wrong called ‘cultural appropriation’. Our goal in this article is to sift through these deliberations and identify what cultural appropriation is, what it is not, and what, if anything might be wrong with it. We begin by explaining why public discourse about cultural appropriation should matter to political theorists of multiculturalism, especially in the anti-immigrant mood that has engulfed many immigrant-receiving countries. We then place cultural appropriation under the umbrella of cultural engagement, before identifying two forms of problematic cultural engagement – cultural offence and cultural misrepresentation – that are often conflated with cultural appropriation. In the next section, we define cultural appropriation as the appropriation of something of cultural value, usually a symbol or a practice, to others. We go on to explain that two additional conditions must be present to define an act of cultural appropriation: the presence of significant contestation around the act of appropriation, and the presence of knowledge (or negligent culpability) in the act of appropriation. Although this account of cultural appropriation is normative, cultural appropriation is often wrong only in a trivial sense. One of the ways it can become more serious is through the presence of what we term ‘amplifiers’. The contextual conditions that can render acts of cultural appropriation more egregious include: the existence of a power imbalance between the cultural appropriator and those from whom the practice or symbol is appropriated; the absence of consent; and the presence of profit that accrues to the appropriator. Ultimately, we find that there are very few instances of seriously wrongful cultural appropriation, and that many of the actions decried as cultural appropriation may be wrongful, but not because they appropriate.
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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 | 0.029 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.121 |
| Scholarly communication | 0.030 | 0.049 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".