Bibliographic record
Abstract
E-government has the potential to change fundamentally the organization of governments, and the governance practices used in relations with citizens and other governments. Legal theory is clearly affected by these changes. Yet there is no rush to publish on e-government in leading legal theory journals, and there is no visible surge in student demand for courses in e-government. Just as only some areas of governments in developed states have taken advantage of new information communication technologies, so only some areas of legal theory have engaged e-government. Issues in Internet governance and personal privacy dominate legal theory’s engagement with e-government, while e-engagement of citizens plays an increasingly important yet still limited role in governments’ interaction with citizens. Yet there are signs that this gentle pace may soon change, as leading jurisdictions approach completion of the first wave of service transformation at the same time as concerns regarding a digital divide recede under the growth of access to new information communication technologies. New opportunities for e-government may soon make e-government’s progress revolutionary rather than evolutionary, and legal theory will be forced to keep pace. Request access from your librarian to read this chapter's full text.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".