The Impact of Meetings on the Network Governance and Mobility of UN Policy Programs on Environment and Education
Bibliographic record
Abstract
Purpose This paper adds to the understandings of how face-to-face meetings contribute to the network governance and global mobility of United Nations (UN) policy programs on environmental and sustainability education (ESE). Design/Approach/Methods Data from interviews with 13 international ESE policy leaders were transcribed, coded, and analyzed for key themes related to the research purpose. Findings The findings indicate that meetings provide an arena for collaboration and influence on UN ESE policy programs, as well as facilitating the impact of the policy programs on UN member country policy. In addition, attending meetings enables the production of network relations that bind ESE policy communities together across distant locations. They are also a venue for the networking labor involved in forging new relationships and facilitating the social learning that supports global policy mobility. Originality/Value This pilot study enriches understanding of face-to-face meetings as a key vector of policy mobility and a significant factor in the overall network governance of UN organizations and their policy programs. We hope the study contributes to the fields of critical policy studies and ESE, as well as to informing policy actors on how important their participation in meetings can be for the network governance and mobility of UN policy programs.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".