A New Middle Ground Between Humanitarian Non-Governmental Organizations and Epistemic Communities? The Cases of the Ottawa Convention and the Convention on Cluster Munitions
Bibliographic record
Abstract
Over the years the advocacy work of research by humanitarian advocacy groups has increased and gained influence on governments. Humanitarian advocacy groups’ research and policy papers on the inhumane effects of anti-personnel landmines and cluster munitions on civilians have successfully contributed to the conclusions of the two international humanitarian treaties, the Ottawa Convention to ban anti-personnel landmines in December 1997 and the Convention on Cluster Munitions in May 2008. Humanitarian advocacy groups persuaded governments through information and knowledge they compiled through their research results and detailed investigations. In this sense, humanitarian advocacy groups have come to assume the roles of an epistemic community, which is a network of knowledge-based experts who provide a government with knowledge and information for problems the government faces. An epistemic community frames the context of an issue, defines state interests, and presents policy options.In this research, I attempt a conceptual elaboration of the merger of international humanitarian advocacy groups and epistemic communities. First, I will clarify two traditionally-held conceptual distinctions between humanitarian advocacy groups and epistemic communities, and then examine whether humanitarian advocacy groups increasingly research-oriented advocacy work makes them closer to epistemic communities. The first distinction is the accessibility to the decision-making process of a government: members of epistemic communities are usually invited by a government to the decision-making process while humanitarian advocacy groups push the government from outside of the decision-making process. The second distinction is the quality of knowledge, information, and analysis. Epistemic community members have professional and scientific expertise based on professional training, hold a common set of causal beliefs, provide a theoretical explanation of causal-effect of an issue, share criteria for policy evaluation, and provide policy options while humanitarian advocacy groups provide rather personal episodes and descriptions of incidents related to the issue, a moral evaluation of an issue, less coherent explanation of the issue, and thus few policy options.I will study how the extent the status and research skills of humanitarian advocacy groups which conducted in-depth research on both anti-personnel landmines and cluster munitions have come closer to those of epistemic communities.
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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 | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.041 | 0.147 |
| Scholarly communication | 0.036 | 0.061 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.017 | 0.015 |
| 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".