Partnering for e‐government: Challenges for public administrators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it