NGO influence at UN negotiations: Institutional efficiency and socially beneficial outcomes?
Bibliographic record
Abstract
There has been a proliferation of non-governmental organization (NGO) participation in international environmental negotiations in recent years, which has come with a great deal of literature on their effectiveness in this role. However, there is no research that connects the effectiveness and influence of NGOs with literature on the role institutional structures play in affecting the possibility to achieve certain outcomes. Bringing together literature on global governance and institutional economics I attempt to draw some conclusions on NGO influence within the United Nations (UN) institutional framework. I gathered information from interviews and observations of the actions of We Canada, a national environmental advocacy NGO that aims to raise awareness about and effect change at the upcoming Earth Summit in Rio. I also observed other NGOs while attending the third intersessional conference for the Rio Earth Summit (Rio+20) in New York. I conclude that NGOs have the ability to influence negotiations within the current institutional framework through agenda setting, forging personal relationships with government officials, partnerships with other NGOs, and educating government delegations and fellow NGOs. Although the goals of these organizations reflective of socially beneficial outcomes, they often lack a focus on institutional reform to increase efficiency of international environmental negotiations.
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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 | 0.062 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".