Factors Determining the Access to E-services of Public Institutions in Sri Lanka: A Case Study of Selected Divisional Secretariat Areas in Ampara District
Bibliographic record
Abstract
Enhancing e-service facilities to the citizens would make it easy for them to access various government and private services. It has currently become an essential aspect of the evolution of public administration. All governments, including those of third world countries, are now trying to improve their e-service delivery. E-service delivery is one of the fundamental mechanisms to enhance quality service delivery with transparency, effectiveness, and efficiency. Sri Lanka has made attempts to deliver e-services in multiple sectors, but many constraints have prevented all citizens from accessing those services. Against this backdrop, this study attempts to investigate the factors that influence the ability of citizens to access the various e-services in selected Divisional Secretariat areas of Ampara district, Sri Lanka. This study was conducted using both qualitative and quantitative research methods during the period from July 2018 to January 2019. The qualitative data were gathered from published books, research articles, and personal interviews, and the quantitative data were gathered through a structured questionnaire and statistical reports of government institutions. The collected data were analysed using both qualitative and quantitative techniques, and results are presented in text, tables and charts format. The findings of the study show that factors such as security, the availability of electronic device facilities, and low cost encouraged citizens to access these services often. Nevertheless, factors like difficulty in understanding e-services and concerns about its security have discouraged people from accessing e-services.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".