Bibliographic record
Abstract
In recent work, Geoffrey Brahm Levey has argued that we can distinguish various schools of multiculturalism on the basis of their methodology (in particular, how they relate theory to practice), and their substantive normative commitments (in particular, their normative commitments regarding liberalism and nationalism). In this article, I offer some reservations about Levey’s analysis. I suggest instead that the various authors Levey discusses in fact share a surprisingly similar diagnosis and remedy. They all seek to expose the selectivity in liberals’ self-understanding of core liberal concepts such as impartiality, colour-blindness, equality, anti-discrimination, secularism, citizenship, civic nationalism, or constitutional patriotism. This selectivity operates in a way that impugns minority claims as always already sectarian, partial and exceptional, while rendering majority claims as always already universal, impartial, and normal. And these authors also broadly agree on the proper remedy to this bias, which is not to reject these core liberal values, but to reinterpret them in a more even-handed way. I offer several examples of how this shared mode of argument is found across the different authors that Levey identifies, and how Levey's attempt to put authors into distinct schools is potentially distorting.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".