Climate Change Subsystem Structure and Change: Network Mapping, Density and Centrality
Bibliographic record
Abstract
Policy capacity in web-based settings is largely the
 product of nodality, which provides centralized actors with
 enhanced opportunities to detect information and affect
 behavior. This paper examines four Canadian virtual policy
 networks (VPN) currently facing policy challenges associated
 with climate change adaptation including finance, infrastructure,
 transportation, and forestry. The four sectors each
 face specific types of challenges that will presumably influence
 government’s policy capacity to respond to climate
 change adaptation, which in turn will affect the state’s nodal
 positioning in the VPNs. At the macro level governing capacity
 will vary considerably among these sectors with some
 more able to affect social behavior and evidence-informed
 learning, while others will struggle to lead policy discourse
 and development. It is hypothesized that the Canadian federal
 government’s nodality, which is shaped by both reputational
 capital and information credibility, will also be influenced
 by the nature of actors involved and the degree to
 which the VPN is internationalized.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".