Bibliographic record
Abstract
Humanitarian organizations, like most virtuous organizations, help us live an ethical life. It is often impossible practically, or prohibitively expensive, to do the right thing on our own. Many of us are deeply concerned about human suffering in places like Darfur, but cannot directly help these distant strangers. Instead the most that we can do is support humanitarian agencies that have the desire and the capacity to act in ways that are consistent with our values. By supporting humanitarian organizations we turn them into our ethical agents. We contribute to humanitarian organizations because we believe both in what they stand for and that they can and will do what they promise. What happens to virtuous organizations when beliefs about their values and their effectiveness are shaken or can no longer be sustained by faith, when their legitimacy and their credibility are questioned? For much of their history humanitarian organizations were credible in large part because they were legitimate, a legitimacy based largely on their social purpose. Acting in the name of humanity and according to universal principles, they were helping the world’s most vulnerable populations. Yet over the last several decades the “virtue” of humanitarian organizations has been challenged; whether their actions are truly consistent with the values of the international community – a question of legitimacy – and whether they can credibly carry out their stated goals – an issue of credibility – came into question.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".