Bibliographic record
Abstract
Far too much of the concern with subnational groups, either long established (and even indigenous) or recently immigrated, is with abstract principles of justice. Far too little of it is about making societies work at all well to give prosperity to everyone and to do so through democratic procedures. Many of the ostensible principles of justice erect barriers between various groups, minority and majority. Assimilationist arguments, pro and con, typically assume assimilation of the minority or new group into the majority or established group. American, Canadian and Australian experience during the twentieth century clearly shows that assimilation goes both ways. Those from Northern European backgrounds in these nations have substantially assimilated with the newly arriving groups of Asians, Latinos and others. There is a substantial shortfall in the assimilation in both directions of blacks in the USA and of indigenous peoples in all three of the former colonial outposts. Brian Barry (2001) is among the few writers who have forcefully taken on these issues with a main eye out for the workability of contemporary societies, especially liberal societies. It would be easy to read him as merely aggressively supporting liberalism over all-comers. But one of his main concerns is with making the societies he addresses reasonably good places. I wish to take up this problem as it is affected by the massive movement of globalization of the past few decades, a movement that is still on the rise.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".