Hybrid network governance: methodologies of studying online and offline networking in global climate education policy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Policy networks connect policy actors across spaces and organizations to advance policy agendas. While much is known about forms of network governance, there is still a lack of research to date on how networks work across online and offline spaces, and the ways that this hybridity of networking arrangements may be influencing policy agendas. In the field of climate communication and education, a range of actors are involved in the network governance of United Nations policy programs through both online and offline networks. In this paper, we examine policy actors’ online and offline hybrid network governance activity. We compare social network analysis of Twitter/X data with broader network ethnography analysis to consider how the focused inclusion of online spaces in network analysis can contribute to a different understanding of the role and functionality of actors in network governance. This paper highlights the value of integrating network ethnography and social network analysis to understand hybrid network governance and actor dynamics in global education policy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it