Partnering for e‐government: Challenges for public administrators
Bibliographic record
Abstract
Abstract: Governments around the world are spending huge sums of money implementing electronic government. Public‐private partnerships with information and communication technology firms have emerged as the vehicle of choice for implementing e‐government strategies. Concerns are raised about the capacity of governments to manage these complex, multi‐year, often multi‐partner relationships that involve considerable sharing of authority, responsibility, financial resources, information and risks. The management challenges manifest themselves in the core partnering tasks: establishing a management framework for partnering; finding the right partners and making the right partnering arrangement; the management of relationships with partners in a network setting; and the measurement of the performance of e‐government partnerships. The article reviews progress being made by governments in building capacity to deal with these core partnering tasks. It concludes that many new initiatives at the central agency and departmental/ministry level seem designed to centralize control of e‐government projects and wrap them in a complex web of bureaucratic structures and processes that are, for the most part, antithetical or, at best, indifferent to the creation of strong partnerships and the business valuethat e‐government public‐private partnerships promise.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.029 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".