Bibliographic record
Abstract
During the last decade, the public policies of many countries have emphasized the need for greater citizen participation in decision-making, and governments have been adopting e-government strategies as a means of not only improving service delivery, but also engaging society and revitalizing democracy. Indeed, many political leaders have been advancing the democratic potential of information and communication technologies (ICTs). British Prime Minister Tony Blair, for example, has stated: “I believe that the information society can revitalize our democracy...innovative electronic media is pioneering new ways of involving people of all ages and backgrounds in citizenship through new Internet and digital technology ... that can only strengthen democracy” (Hansard Society, 2004). Similarly, former United States President Bill Clinton stated that ICTs would “give the American people the Information Age that they deserve—to cut red tape, improve the responsiveness of government toward citizens, and expand opportunities for democratic participation” (Prins, 2001, p. 79). In Canada, former Prime Minister Paul Martin also argued, along the same vein, that people need to be brought into the decision-making process if the country is to have the kind of future that it needs, indicating that ICTs are a useful means of achieving this goal (Speech to the 2003 Crossing Boundaries Conference, Ottawa Canada).
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".