A Multicriteria Evaluation of Local E-Government in Terms of Citizen Satisfaction
Bibliographic record
Abstract
Any strategy to improve local public administration gives paramount importance to the development of electronic administration (E-Local Government) to facilitate interaction with citizens promoting efficiency, local participation, and sustainable development. This explains the numerous contributions to evaluating websites of local authorities, but they do not focus on modeling the citizen satisfaction induced by their use. This is why three research questions are addressed by this paper: how can such satisfaction be modeled, which attributes be considered and described and how to assess the relative importance assigned by citizens to each attribute? The level of satisfaction is found to depend on the easiness of use of each site and of the available functionalities to share information, to provide services, and to promote participation. A model based on the Multiattribute Theory and using the OptionCards method is developed to estimate the relative importance assigned by each citizen to each attribute and it is successfully applied to a focus group. The answers presented by the authors to these questions are applied to a sample of Portuguese websites allowing their benchmarking and the identification of a road map for im-provement. This instrument is applied to a set of Portuguese municipalities revealing a high level of disparity and confirming how important can be its application to assess inter-municipalities benchmarking, to diagnose their LGS major shortcomings and to support the design of a road map for improvement.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".