E-Governance and Government On-line in Canada: Partnerships, People and Prospects
Bibliographic record
Abstract
The objective of this paper is to examine the capacity of the Canadian federal government to effectively harness information technology (IT) as an enabling force in its efforts to meet the present and emerging challenges of a digital age. The main thesis of this paper is that this necessary transformation in public sector governance and accountability is likely to be blocked by an administrative culture that may be ill suited for a digital world. In terms of how governments respond, our two sets of explanatory factors will be determinant. First, partnerships, and the emergence of new collaborative dialogues within government, between governments, and across sectors are a critical dimension. The second, and quite related variable lies in the necessary leadership of people -new skill sets, and new leaders will be required to both empower knowledge workers and defend experimental action. Yet, it is not only the skills composition of workers altering in a digital era, but rather the broader transformations of both everyday and organizational life that are also at play. In this sense, digital government must reposition itself to become an engaged and constructive partner in shaping the new governance patterns that will otherwise render it rudderless. Government must produce a new "culture" in order to harness the enormous potential of digital government. © 2001 Elsevier Science Inc. All rights reserved.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".