The Misrecognised as the Least the Advantaged Citizens in Plural Democracies
Bibliographic record
Abstract
John Rawls’s “Justice as Fairness” is the most systematic attempt in recent decades to provide a liberal grounding for justice in plural democratic societies. Rawls argued that social and economic inequalities are justifiable only if they are to the advantage of society’s least-advantaged members. Rawls argued that the least-advantaged position in society was occupied by the citizen with the lowest expectation for primary social goods (all-purpose means like income and opportunity). This paper argues that the least-advantaged citizens, in part, are those whose identities are misrecognised. Misrecognition of identity can cause harm; it can restrict the agency and opportunity of the misrecognised. Minority identity groups (whose identities are often misrecognised) do not do as well as others citizens in social, economic and political terms. This paper argues that the misrecognition of identity constitutes unreasonable democratic practice because it can harm members of minority identity groups.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".