What’s It Worth to You? The Questionable Value of Instrumentalist Approaches to Ethnic Identification
Bibliographic record
Abstract
The assertion that material interests underlie ethnic identification is central to instrumentalist approaches to ethnicity. However, recent approaches—circumstantialism and constructivism—refine instrumentalism, addressing posited shortcomings, including an examination of the contexts and conditions in which interests and identities are expressed and constructed. Nevertheless, these later approaches explicitly or implicitly reproduce instrumentalism’s basic material premise. Using survey data from the multiethnic country of Mauritius, I examine this premise by exploring the relationship between economic instrumentalism and ethnic identification within and across ethnic groups. I find limited support for an instrumentalist approach to ethnic identification as this approach explains only a modest amount of variance in ethnic identification and is insufficient for explaining the significant differences that emerge in the relationship between economic instrumentalism and ethnic identification across ethnic groups. Consequently, it is argued that current instrumentalist approaches provide a deficient account of ethnic identification and related group processes.
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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 | 0.024 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".