Parameters Influencing Citizens’ Levels of Satisfaction: Soft Indicators of ‘Good Governance’
Bibliographic record
Abstract
Previous studies highlighted the importance of soft indicators (e.g., quantitative sociological survey) influencing citizen satisfaction towards municipal service quality. Citizen satisfaction assessments have raised concerns over numerous independent parameters such as gender, age, and education on satisfaction levels toward administrative performance. It is also crucial to underline that the application of sociological survey for improving service quality is not well understood by municipal officers or scholars. To obtain substantial combined multiple indicators of service quality, it seems rationale to reconnoitre numerous parameters of citizen satisfaction and quantitatively investigate impacts of independent variables (e.g., gender, age, education) on corresponding satisfaction levels by using some advanced statistical tools. In this sociological assessment, a targeted population was constructed of Bangkok Metropolitan administration (BMA) stakeholders (n = 38,500), which are as follows: - Bangkok residents in 50 districts under the governance of BMA - Board committee, executive directors and general staff of 27 BMA offices This multiple dimensional analysis sociological survey data indicates that gender, age, and education play some important roles in governing municipal citizen satisfaction levels. Overall, the knowledge of relationship between citizen satisfaction levels and independent parameters can enhance the service quality of municipal administration.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".