New-media social networks, issue networks, and policy communities: Getting and using power
Bibliographic record
Abstract
This PAR project used applied communications to get and use power to influence public policy. Informed by social and policy network theories, the method used Facebook as an organizing tool to create and position a recreation issue network in tension with an environmental policy community, exploring the concepts of layering, conversion, exhaustion, policy image, and venue change in an effort to influence policy. The introduction of a new-media social network as a competing influence in a policy network was an innovation, and demonstrated that the “strength of weak ties” may have implications for policy-making. The study concluded that a Facebook group was an efficient and effective organizing tool, capable of organizing an issue network and disrupting the status quo; however, the tightly coupled nature of a policy community makes it highly resilient to outside influence and an issue network may not gain sufficient influence to change policy. Keywords: Facebook, new-media social network, policy community, issue network, policy image, venue manipulation, layering, conversion, exhaustion
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".