Policy and Politics on the Web: Virtual Policy Networks and Climate Change
Bibliographic record
Abstract
This paper analyses policy information on the Web to understand how the hyperlinked organization of webpages, produced by real world, web-enabled policy communities, influences the structure and content of the Web’s information supply. These virtual networks of information will be referred to as virtual policy networks (VPN), which are defined as observable patterns of relations among web-enabled policy communities. The organization of virtual teams, social networks and online communities is well documented; however, similar considerations of real world policy communities that are fully established, and then become web-enabled are sparse. This project takes tentative steps towards addressing this dearth in the literature by examining the networked relations of the Canadian climate change VPN. The key research question addressed in this project is whether or not the Canadian climate change VPN is structurally and relationally analogous to the real world climate change policy community. As virtual policy works are produced by real world web-enabled policy communities it is hypothesized that the Canadian climate change VPN will mimic the real world policy community’s patterns of communication and organization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".