Enhancing Open Government Data Quality: A Quantitative Evaluation Assessment for Cross- Jurisdictional Open Data Programs in Waterloo Region
Bibliographic record
Abstract
This study builds on the previous research for identifying the current issues and gaps existing for the cross-jurisdictional data quality of the open data programs in the Waterloo region, not only as the governments in the Waterloo region have a unique two-tier municipalities structure, but also how the four municipalities the City of Waterloo, the City of Kitchener, the City of Cambridge and the Region of Waterloo shares one same data portal. The goals of this study are to understand what data quality metrics are important for the quality of open data, and how an evaluation tool can be created to effectively measure the data quality for the open data in the Region of Waterloo. A quantitative approach was used for measuring individual metrics of the data quality dimensions such as completeness, timeliness, metadata, and usability. The results show there are still a lot of improvements that can be made by the lower-tier municipalities on quality assurance, regular maintenance, and updates of data policies. The results also indicated that upper-tier municipalities like the regional government of Waterloo can take the leading role in improving the overall data quality of open data programs by creating open metadata and data standards. Additionally, the results also note the insufficient of both current and previous research and provide suggestions for future studies in similar settings.
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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 | 0.077 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".