Increasing E-Trust in E-Government Services: A Case Study on The Users of Internet Tax Office
Bibliographic record
Abstract
In the last quarter century, governments around the world have been working to capture the vast potential of the Internet to improve government processes. Turkish government has increasingly benefited from information technology to enhance their services, known as electronic government (e-government). However, the success of these efforts depends, to a great extent, on how well the targeted users for such services, citizens in general, make use of them. Electronic tax payment system is one of the critical e-government services, which assists tax payers in paying their tax debts electronically each pay period. Since citizens’ acceptance of electronic tax payment system is influenced by their trust to this system, there is a need to understand the factors that predict the users’ trust on internet tax office. For this reason, the purpose of the presented study was to identify what factors could affect the citizens’ trust in e -government services. The study was conductedby surveying 426 citizens from all Turkish regions. The theoritical and practical implications of the study are discussed in the paper.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".