Bibliographic record
Abstract
This chapter explores how to formulate an ICT-enabled eGovernance action plan, including the necessary components of (a) a knowledge management (KM) strategy, and (b) the adoption of a culture of learning organization (LO) behavior. This strategy is based on lessons learned from a model designed and tested on data from 140 Slovenian public agencies. Slovenia, a small transition economy newly admitted to the European Union, faces both its own demands and the demands of the EU for good governance. Slovenia offers lessons relevant for both developed and developing countries. There are three progressively complex stages when integrating information and communication technologies (ICT) into the operations of government. They start with the elementary process of integrating ICT into previously paper based governmental administrative systems (iGovernment), proceed to the online provision of government services to others (e-government), and finally arrive at online efforts to enhance accountability, consultation and transparency as part of good governance (eGovernance). This chapter concludes with the argument that successful eGovernance works hand-in-hand with e-democracy, whereas failed eGovernance will position eDemocracy as a force in opposition to the behaviour of Government.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.021 |
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".