The Case for Local Ethics Oversight in International Development Research
Bibliographic record
Abstract
This paper argues that international development research should be submitted to the oversight of research ethics committees from the countries where data will be collected. This includes research conducted by individuals who may fall outside the jurisdictions of most ethics guidelines or policies, such as individuals contracted by non-governmental organizations. The argument is grounded in an understanding of social justice that recognizes that not seeking local ethics approval can be an affront to the decolonization movement, and may lead to significant direct harms to participants. Local ethics oversight can help ensure projects appropriately take into consideration local laws, regulations, priorities and context. For example, a local research ethics committee may be in a better position than a foreign one to assess whether any given proposed project carries context-specific risks. In addition, submitting to a local research ethics committee is to acknowledge the legitimacy of local authorities, thereby taking a stance against the history of colonizing disempowerment. Local oversight is a mechanism to increase the accountability of researchers working abroad: if respect for local authority and tailoring to local context are to be upheld, there must be mechanisms to ensure that research that does not meet these requirements does not proceed. Objections based on the limited oversight capacity in some countries and on concerns related to the politicization of the review process are discussed. Finally, the roles and responsibilities of the various stakeholders in the implementation of greater local ethics oversight are laid out.
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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 | 0.422 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.021 | 0.120 |
| Scholarly communication | 0.034 | 0.034 |
| Open science | 0.005 | 0.037 |
| Research integrity | 0.019 | 0.037 |
| Insufficient payload (model declined to judge) | 0.005 | 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".