Can the quality of social research on ethnicity be improved through the introduction of guidance? Findings from a research commissioning pilot exercise
Bibliographic record
Abstract
As the volume of UK social research addressing ethnicity grows, so too do concerns regarding the ethical and scientific rigour of this research domain and its potential to do more harm than good. The establishment of standards and principles and the introduction of guidance documents at critical points within the research cycle might be one way to enhance the quality of such research. This article reports the findings from the piloting of a guidance document within the research commissioning process of a major funder of UK social research. The guidance document was positively received by researchers, the majority of whom reported it to be comprehensible, relevant and potentially useful in improving the quality of research proposals. However, a review of the submitted proposals suggested the guidance had had little impact on practice. While guidance may have a role to play, it will need to be strongly promoted by commissioners and other gatekeepers. Findings also suggest the possibility that guidance may discourage some researchers from engaging with ethnicity if it raises problems without solutions; highlighting the need for complementary investments in research capacity development in this area.
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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 | 0.506 | 0.730 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".