The Problem of Doing Good in a World That Isn't: Reflections on the Ethical Challenges Facing INGOs
Bibliographic record
Abstract
One great virtue of bringing together moral theorists and representatives from international nongovernmental organizations (INGOs) in a project like this one is that each group can potentially learn from engagement with the other. Moral theorists are trained to think carefully about the ways in which moral claims can be advanced and defended, to distinguish good arguments from bad ones, to clarify terms, to identify presuppositions, to examine the relationships between various elements in a moral position, to expose contradictions and inconsistencies, and to present accounts of moral views that are coherent. So people working in INGOs might gain by engaging with the kinds of abstract and systematic thinking that are the theorist's stock in trade. This could help those in INGOs to reflect more deeply about the underlying moral principles that they want to guide their actions and about whether the courses their organizations pursue really live up to their own principles.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".