Accountability and monitoring government in the digital era: Promise, realism and research for digital‐era governance
Bibliographic record
Abstract
Abstract Furthering the accountability of elected governments and the public administration apparatus which serves them is a fundamental principle of democratic societies. Over the last fifty years, there have been significant debates about how to operationalize and balance the principles of accountability in our federal governance system. The emergence and proliferation of Web 2.0 capabilities and advocates for their use in government has led to new rounds of experimentation, initiatives and reform under the banner of Government 2.0 in many jurisdictions. This article surveys the Canadian and international literature on accountability in the digital era, including contributions from scholars with interests in information technology, transparency and digital culture, to identify whether Canada is lagging or leading international contributions in this area. It sets out a research agenda inspired by the concepts of interactive, dynamic, and citizen‐initiated accountability (Schillemans, Van Twist, and Vanhommerig ).
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.073 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".