No Room For Humanitarianism in 3D Policies: Have Forcible Humanitarian Interventions and Integrated Approaches Lost Their Way?
Bibliographic record
Abstract
This paper will review the evolution of integrated and 3D approaches and seek to highlight the different responses to such approaches shown by classic humanitarian organizations and multi-mandate development organizations. By providing an overview of past forcible humanitarian interventions and with a particular focus on Afghanistan, we will trace the practical and ethical challenges faced by aid agencies attempting to maintain programming in such contexts. In so doing it will be suggested that the 3D approach emphasizing coherence between different instruments, while motivated by good intentions, has resulted in humanitarian and development aid programming becoming subordinated to political interests in counterproductive ways. In fact, in Afghanistan the co-optation of soft power for political and military ends has led to reduced humanitarian assistance for populations in danger and to increased insecurity for humanitarians trying to assist them – thereby effectively exposing clear limits to the deeper integration strategies currently being promoted for stabilizing failed states.
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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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".