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. Moral theorists have much to gain as well by engaging with people from INGOs. In contrast to organizations like corporations and political parties for whom ethical considerations normally function only as constraints on the pursuit of the organization's primary goals (if ethical considerations play any role at all), INGOs like the ones connected to this project have ethical concerns as their primary goals. Whatever the specific formulation of their mission – social justice, human rights, and so on – their raison d'être is the promotion of some moral good.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.050 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".