Rethinking Research Ethics in the Humanities: Principles and Recommendations
Bibliographic record
Abstract
This AHRC-funded report is designed to stimulate reflection and discussion about ethical issues that could arise in qualitative, Humanities-based research designs that might be considered ‘risky’. The report can be used at project meetings; by University Research Ethics Committees (URECs), College Research Ethics Committees, and other governance bodies; and in discussions with project stakeholders. It is also designed to help postgraduate, early-career researchers, and PhD supervisors navigate key issues pertaining to risky qualitative research, and to provide additional readings and precedence in developing applications for ethical review. The report is organised thematically and proposes a series of principles for reforming ethical review in this space, as well as recommendations for URECs, governance bodies, and funders. The themes arising may not be applicable to all qualitative research designs, and the specific methods and context of the research will need to be reflected upon when using this report. Different types of methodologies, participants, stakeholders and local contexts will require different ethical-approval processes that use disparate forms and procedures. The reflection that this report intends to stimulate should be promoted by and among all those involved in the design and conduct of the research, including wherever possible with participants and their communities. How to cite this report: Kasstan, Jonathan R., Pearson, Geoff & Victoria Brooks (2023): Rethinking Research Ethics in the Humanities: Principles and Recommendations. doi.org/10.34737/w36yq.
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 | 0.557 | 0.421 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.014 | 0.069 |
| Scholarly communication | 0.044 | 0.051 |
| Open science | 0.010 | 0.028 |
| Research integrity | 0.024 | 0.055 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".