Bibliographic record
Abstract
This review essay focuses on the concept of inequality in Daniel Dorling’s The Population of the United Kingdom and Reza Hasmath’s A Comparative Study of Minority Development. The review discusses the structure of social inequality and the principal social cleavages that are shown in both books. The books raise different issues of ethnicity, visible minority (Hasmath) and class, and life outcomes (Dorling) as they relate to the broader processes and consequences of human efforts to stratify the social world. Whether those investigations are done at the micro or macro level, they provide strong empirical evidence that social and cultural standards will be never seen as adequate as long as great social inequalities prevail. This essay also discusses the problematic use of social inequalities for empirical research. Dorling is especially sensitive to inequalities and has a strong interest in uncovering those ‘deep structure[s]’ of social differentiation that are concealed from ordinary view. Broad categories, such as poor/affluent or minority/majority, with masking of in-group differences within categories, are appropriate for a rough scan of inequalities. However, this crude classification is inappropriate for a precise summary measurement of inequalities among social/ethnic distributions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.032 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".