National Information Policy developments worldwide I: electronic government
Bibliographic record
Abstract
A review of recent Government initiatives in the area of e-Government based upon a review of the literature is presented. The desk research covered the period 1997 to 2001, and covered a number of major countries, including Canada, USA, Member States of the European Union, South Africa, Hong Kong, Australia and New Zealand. The UK was not included in the survey. The targets set by Government are often vague, and few governments seem to have addressed in any thoughtful manner the problems citizens might have with use of technology. An approach along the lines of ‘this is bound to happen’ rather than ‘what sort of society do we really want?’ is a common feature amongst all the approaches examined. The risks of enhancing the digital divide are also rarely explicitly addressed. Comments regarding good initiatives that offer models for other countries to adopt are made. The emergence of government portals is without doubt the most significant development. These provide the facility for personalization by the user. The New Zealand Government’s efforts to ensure that its web sites are useful for citizens who have difficulty spelling and the Canadian Government’s use of minority languages are also noteworthy. The leading countries are Australia, New Zealand, USA and Canada. The Australian Government’s e-procurement strategy is a role model for the future.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".