The web structure of e-government - developing a methodology for quantitative evaluation
Bibliographic record
Abstract
In this paper we describe preliminary work that examines whether statistical properties of the structure of websites can be an informative measure of their quality. We aim to develop a new method for evaluating e-government. E-government websites are evaluated regularly by consulting companies, international organizations and academic researchers using a variety of subjective measures. We aim to improve on these evaluations using a range of techniques from webmetric and social network analysis. To pilot our methodology, we examine the structure of government audit office sites in Canada, the USA, the UK, New Zealand and the Czech Republic.We report experimental values for a variety of characteristics, including the connected components, the average distance between nodes, the distribution of paths lengths, and the indegree and outdegree. These measures are expected to correlate with (i) the navigability of a website and (ii) with its "nodalityö which is a combination of hubness and authority. Comparison of websites based on these characteristics raised a number of issues, related to the proportion of non-hyperlinked content (e.g. pdf and doc files) within a site, and both the very significant differences in the size of the websites and their respective national populations. Methods to account for these issues are proposed and discussed.There appears to be some correlation between the values measured and the league tables reported in the literature. However, this multi dimensional analysis provides a richer source of evaluative techniques than previous work. Our analysis indicates that the US and Canada provide better navigability, much better than the UK; however, the UK site is shown to have the strongest "nodalityö on the Web.
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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 | 0.114 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".