Bibliographic record
Abstract
As information cascades across the Internet and human communication patterns are transposed into computer-mediated environments, governments around the globe race to garner the benefits of ICTs and Web-based communications (Bannister & Walsh, 2002; Chadwick & May, 2002; Falch & Henten, 2000; Heeks, 2002; Ma, Chung, & Thorsona, 2005). Increasingly sophisticated user-citizens can now access numerous electronic services, collect policy-relevant information, and communicate with governments through electronic channels (Dahlberg, 2001; Lenk, 2003). Contemporary e-government, while variable across states, has evolved significantly in the last decade, pursing increasing transparency and accountability through the implementation of various e-government measures (Jaeger & Thompson, 2003; Reddick, 2005). While theoretical contentions concerning the authenticity of e-democracy have yet to abate, collections of policy actors that seek entrance and participation in the public policy process have also emerged online (Chadwick & May, 2002; Della Porta & Mosca, 2005; Klein, 2002). This article considers these online policy communities.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".