Advancing Public Trust Relationships in Electronic Government: The Singapore E-Filing Journey
Bibliographic record
Abstract
E-governments have become an increasingly integral part of the virtual economic landscape. However, e-government systems have been plagued by an unsatisfactory, or even a decreasing, level of trust among citizen users. The political exclusivity and longstanding bureaucracy of governmental institutions have amplified the level of difficulty in gaining citizens' acceptance of e-government systems. Through the synthesis of trust-building processes with trust relational forms, we construct a multidimensional, integrated analytical framework to guide our investigation of how e-government systems can be structured to restore trust in citizen-government relationships. Specifically, the analytical framework identifies trust-building strategies (calculative-based, prediction-based, intentionality-based, capability-based, and transference-based trust) to be enacted for restoring public trust via e-government systems. Applying the analytical framework to the case of Singapore's Electronic Tax-Filing (E-Filing) system, we advance an e-government developmental model that yields both developmental prescriptions and technological specifications for the realization of these trust-building strategies. Further, we highlight the impact of sociopolitical climates on the speed of e-government maturity.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".