LOOKING FOR MONTREAL DIGITAL CITIZENS: FOR WHO ARE OPEN DATA MADE?
Bibliographic record
Abstract
Municipalities' development of open data portals is part of a political drive to bring citizens and local governments closer together. However, despite significant investments, these initiatives rarely find their audience, because they respond primarily to an imperative of openness that places them in a logic of supply rather than demand. Also because citizens have a minimal understanding of the concrete implications of open data. In this context, one can legitimately ask for _whom_ are these open data portals created. Using the City of Montreal (Quebec, Canada) as a case study, we provide a nuanced answer to this question. If the socio-technical device set up seems to meet the needs of an "imagined" public more technophile and entrepreneurial than most citizens are, this discrepancy is perceived and countered by many processes. Our longitudinal study over a decade allows us to detect a co-construction in tension of the web portal. On the one hand, we find preoccupations with municipal prerogatives, administrative routines, and open-data movement ideals - sometimes mixed with territorial branding strategies. On the other, we find attention to citizen appropriation, the search for a better understanding of empirical audiences and a willingness to design the portal for the nebulous public of "Montrealers". We base our analysis on a three-tiered data: the _imagined_ publics in municipal discourses, the publics _configured_ by the digital device, and the publics _constructed_ through the actions and strategies of the actors. We discuss findings on the portal’s evolution, and on the clash between imaginary worlds.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".