Humanitarian Military Interventions in the Decade 1990-2000: Remodelling the Concepts of Impartiality and Political Independence.
Bibliographic record
Abstract
The concept of Humanitarian Military Interventions has become a core issue within the international community since the 1990s. Human rights violations carried out on a massive scale are no longer perceived as purely domestic concerns but are now recognized as a central concern of the international community. This study of four cases of HMI -Somalia, Rwanda, Haiti and East Timor- is intended to shed the light on two political factors that play a determining role in HMI: the national interests of the interveners and the level of neutrality of the operations. I argue that the level of success of HMI is highly dependent on the presence of national interests in the region for the interveners and a low level of neutrality. This thesis also reflects on the ongoing challenges facing the international community regarding the most efficient ways to address massive human rights violations and presents suggestions towards addressing them.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".