Virtual Policy Networks in Forestry and Climate Change in the U.S. and Canada: Government Nodality, Internationalization and Actor Complexity
Bibliographic record
Abstract
Abstract The Internet and the Web have changed policy formulation processes. The Web has increased the potential for governments to use information to manage the policy process and direct policy outcomes. It is argued that understanding the macro impacts of web‐based information and measuring the performances of online public sector information activities is vital to improving governments' web‐based capacity. This paper examines government nodality, internationalization, and actor composition in nine virtual policy networks to explore similarities and differences in online policy activities across different regions. The nine networks analyzed are issue specific, focused on forestry and climate change in four western Canadian provinces (Manitoba, Saskatchewan, Alberta, and British Columbia) and in five western U.S. states (Washington, Oregon, California, Montana, and Colorado). Discussion is focused on how specific contexts around governing priorities, political responses and issue‐specific policy problems shape the nature of virtual web‐based information networks.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".