Towards Online Local Government Elections
Bibliographic record
Abstract
Local governments around the world are already providing services to the public through the Internet. These range from rate information to payment of traffic fines and tourism promotion. Overall, the provision of online services is collectively referred to as ‘e-local government’. The purpose of e-local government initiatives is to increase the availability of government to its citizens, provide better services and enhance participation in local democracy. The last of these categories, increasing participation in local democracy through e-government, is the domain of online elections. The primary goal of this study was to determine the current progress towards online local elections in New Zealand. We developed and used automated content categorisation software to analyse local government body web sites. The results of our content analysis over 78 local government bodies were then compared with other research in the area and local government online election strategy. Our findings showed that over a quarter of local government bodies do not provide any election-related information through their web site. The remainder of the sites offer varying levels of election-related content quality in four main categories – general election information, candidate nomination information, voter registration and election results. We discovered that to date none of the New Zealand local councils offer online voting services and on average, local government web sites offer a low quality of election-related content. A number of “best-of-breed” sites that could serve as an example for local councils wishing to improve upon the quality of their offerings are identified and described.
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".