Citizen Engagement: the next horizon for digital government
Bibliographic record
Abstract
'Good government is expensive, bad government is unaffordable' A mantra for governance in the twenty first century Governments at all levels are facing new challenges to their legitimacy. Calls for greater transparency and participation are heard not just by elected officials, but also in corporate headquarters. At every stage of the policy process, digital communications can either enhance or inhibit democratic accountabilities. In the best of worlds, participation becomes a design element, and consultation a fine art, digitally documented. The other extreme, lack of transparency, can also be designed for, but with repercussions. This chapter looks at the ways governments are repositioning themselves to adapt to an information age. Many agencies have only recently realised that delivering information and services online is just the start of a long pathway to meeting expectations of the new ‘digerati’. Increasingly responsive services are one important form of adaptation, as these open the doors to iterative feedback and development. The smarter ‘networked ’ officials become increasingly facilitators and centres of fluid change, rather than stolid determiners of static policies. Such agility requires speed and scope, assisted by many fingers flying across keyboards. Canadian work on the ‘networked ’ model of
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.029 | 0.039 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.031 | 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".