The Case for Local Ethics Oversight in International Development Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.069 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.022 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it