Bibliographic record
Abstract
The rash of humanitarian interventions since the end of the Cold War has posed serious analytical problems for International Relations (IR) scholars. Traditional security scholars have struggled to understand the nature of ‘humanitarianism’ as an interest, often with the result that they simply discount it and emphasise other possible motivations for intervention. In these analyses, the intervention in Somalia is explained as an effort to export US values, intervention in Haiti was about controlling unwanted refugee flows, interventions in Bosnia and Kosovo are explained by the need to protect NATO's (North Atlantic Treaty Organization) credibility and maintain stability in Europe. Humanitarianism was only window-dressing in every case. Constructivists, legal scholars and an increasing number of policy analysts have taken humanitarianism more seriously as a source of state action. They point to the increasingly dense network of human rights norms, law and transnational activist groups that all persuade (or coerce) policy makers and publics to support these interventions. The analytic problem for this group has been to understand why humanitarianism produces the sorts of actions it does in world politics and why its influence and effects seem so inconsistent and varied. Humanitarian concerns do not always produce interventions (as the Rwanda case makes painfully clear) nor do they produce interventions of the same kind.
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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".