Examining the Role of Trust and Informal Communication on Mutual Learning in Government
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
Although public agencies must mutually coordinate climate policy and other complex environmental issues, the extent and relative importance of informal networks and different dimensions of trust to the process remains underresearched. Addressing this, we conducted surveys and interviews with civil servants from numerous agencies and three levels of government working on climate change–related policy in the state of New York. We examined the effect of two network properties on mutual learning on climate change–related issues: the extent to which interagency communication takes places through formal and informal channels, and the distribution of two dimensions of trust (“fair play” and “relational comfort”) across the network. Our analysis revealed that formal communication among staff at different agencies was utilized more often than informal and that interagency relationships were more characterized by a feeling of “fair play” than by “relational comfort,” yet informal communication and Relational Comfort were the most important in facilitating interagency collaboration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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