Bibliographic record
Abstract
Broad-ranging and comprehensive, this completely revised and updated textbook is a critical guide to issues and theories of ‘race’ and ethnicity. It shows how these concepts came into being during colonial domination and how they became central – and until recently, unquestioned – aspects of social identity and division. This book provides students with a detailed understanding of colonial and post-colonial constructions, changes and challenges to race as a source of social division and inequality. Drawing upon rich international case studies from Australia, Guyana, Canada, Malaysia, the Caribbean, Mexico, Ireland and the UK, the book clearly explains the different strands of theory which have been used to explain the dynamics of race. These are critically scrutinised, from biological-based ideas to those of critical race theory. This key text includes new material on changing multiculturalism, immigration and fears about terrorism, all of which are critically assessed. Incorporating summaries, chapter-by-chapter questions, illustrations, exercises and a glossary of terms, this student-friendly text also puts forward suggestions for further project work. Broad in scope, interactive and accessible, this book is a key resource for undergraduate students of 'race' and ethnicity across the social sciences.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".