Bibliographic record
Abstract
This volume has evolved out of a special issue of the Review of Social Economy that we edited in 2000.1 Our interest in understanding patterns of ethnic economic inequality across countries prompted us to look for comparative studies and it turns out there is a small but growing body of work (see Nesiah 1997; Darity 1998; Darity and Deshpande 2000; Darity and Nembhard 2000). Eight general findings have emerged from these studies (Table 1.1 summarizes some of these points for a selected group of countries with welldocumented inter-group disparity): 1 Countries with a lower general level of income inequality do not necessarily display lower levels of inter-group inequality. For instance, both Brazil, South Africa and Malaysia with high overall inequality levels on the one hand, and Canada, India, Australia and Israel with low overall inequality levels on the other have substantial inter-group inequality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.009 |
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".