E-Government as Collaborative Governance
Bibliographic record
Abstract
In discussions on e-government, terms such as “seamless” and “joined-up” are often deployed in reference to restructuring the public sector for more effective performance. There is a critical link between delivering services online in a more client-centric fashion and government organization. This critical link often involves new coordinating mechanisms (i.e., new forms of governance) that are more collaborative than before — thus, e-government becomes collaborative government — and as such, many challenges present themselves. In government, however, collaborating is both complex and contentious, as much of public management has traditionally been premised on a command and control regime, where clear structures and rules dictate the behavior of public servants. The contentious nature of collaboration is also amplified by the political nature of government activity, and the difficulties in coordinating activities horizontally across traditional organizational units: there are structural, accountability and cultural dimensions of such coordination. E-government must be built on a fluid and constantly adapting of collaborative governance systems that respond to the twin challenges of external alignment and internal integration and cooperation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".