Guyana - How Do You Know Where to Get the Information You Need? Determining Ethics Approval Requirements in a Developing Country
Bibliographic record
Abstract
Determining the process for obtaining local research ethics approval, or whether such a requirement even exists, may not always be straightforward in the context of some developing countries where such information may not be easily accessible to overseas researchers. How far do a researcher's ethical obligations extend in determining whether there is a requirement for local research ethics approval, and what form this would take (e.g., institutional or centralised ethics review)? In other words, how far should a researcher be expected to go in seeking out local ethics approval, especially where such information is not readily available? As part of this discussion, this case study describes the steps that I took as the principal researcher, before I was able to reasonably conclude that no national ethics approval requirement existed in Guyana for my particular research, which involved interviewing justice service providers about the implementation of Guyana's Domestic Violence Act. Drawing on this experience, I discuss various considerations that an international development researcher should bear in mind when planning and conducting research that seeks to meet leading international norms of research ethics.
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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 | 0.040 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".