Social research for a multiethnic population: do the research ethics and standards guidelines of UK Learned Societies address this challenge?
Bibliographic record
Abstract
There is increasing recognition in the UK that social science research should generate an evidence base that reflects the ethnic diversity of the population and informs positive developments in public policy and programmes for all. However, describing and understanding ethnic diversity, and associated disadvantage, is far from straightforward. In practice, the ethical and scientific arguments around whether and how to incorporate ethnicity into policy-relevant social research are complex and contentious. In particular, untheorised or insensitive inclusion of data on ethnic ‘groups’ can have negative consequences. The present investigation begins to explore the extent to which social scientists have access to advice and guidance in this area of research. Specifically, the paper examines how ethnic diversity is explicitly or implicitly considered within the research ethics and scientific standard guidance provided by UK social science Learned Societies to their members. The review found little in the way of explicit attention to ethnic diversity in the guidance documents, but nevertheless identified a number of pertinent themes. The paper compiles and extrapolates these themes to present a tentative set of principles for social scientists to debate and further develop.
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 | 0.338 | 0.500 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.046 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.022 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".