MUNICIPAL OPEN DATA PORTALS: THEIR IMPLEMENTATION IN THE FACE OF A MUNICIPALITY’S POLITICAL AND TERRITORIAL REALITY
Bibliographic record
Abstract
Abstract. Municipal open data portals have been criticized for their inability to fulfill the promises of transparency, citizen participation and economic development that are supposed to accompany data release. Based on an analysis of certain aspects of the City of Montréal’s open data portal and interviews with reusers of these data, we show that the limitations observed stem – at least in part – from an absence of consideration of the municipality’s political and territorial reality. Three facts contribute to this absence: 1) the Montreal open data portal was designed as a public service; 2) it was created upstream, and not based on the identification of possible needs of the population or the territory; and 3) the relevance of the published datasets raises questions with respect to the promises made. These elements invite us to better link open data portals to objectives and needs that are first and foremost local, while inserting them into a broader framework for achieving the initial democratic and economic promises.
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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 | 0.048 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".