Bibliographic record
Abstract
Michael Banton was one of the founders of the study of ethnic relations in Britain, including what might be called 'the Bristol School of Ethnic Relations'.So, most my contemporaries would have encountered his work as students in the 1970s and 1980s.I came to this field of study-having in the 1970s and 1980s studied political philosophy or been out of academia altogether-in about 1987.One of the first things that got me interested in Michael Banton was when I read his statement: 'In my view the lack of an agreed nomenclature is one of the most revealing features of racial and ethnic relations in Britain today' (Banton, 1987, p. 175).That he could write this in 1987 must have meant that he felt that his arguments over many years resisting the inclusion of Asians under the category 'Black' (e.g., Banton, 1976) had not been in vain.In fact, they had been far from successful; indeed, it would be fair to say that in 1987, there was an agreed nomenclature in the public discourse of race.Those who believed that for reasons of tidiness as well as effective anti-racism, the way forward lay in establishing the hegemony of the term 'Black' seemed by that point to have won the day.The term was not quite so securely established that academic or policy document writers felt no need to justify the use of 'Black' to mean all non-white minorities.Where such a justification was felt necessary, even in the case of writers who were not wholly sympathetic to such reduction, it usually consisted of a footnote simply explaining that the usage of the all-inclusive Black was now an established fact (e.g., Nanton, 1989: note 1;
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".