Integrating Across Sustainability, Political, and Administrative Spheres: A Longitudinal Study of Actors’ Engagement in Open Data Ecosystems in Three Canadian Cities
Bibliographic record
Abstract
Over the last decade, cities around the world have embraced the open data movement by launching open data portals. To successfully derive benefits from these initiatives, engagement from a wide range of individual and organizational actors is needed. These actors undertake activities supporting data publication and dissemination within open data ecosystems. We aim to enhance the IS community’s contribution to the open data movement by conducting a longitudinal, qualitative archival analysis of open data initiatives in three Canadian cities: Edmonton, Toronto, and Montreal. Combining two complementary models of open data and information ecosystems, we explore how actors engage within and across the sustainability, political, and administrative spheres to influence the success of open data initiatives. Our findings suggest most actors operate within a single sphere, but some are able to integrate across two or all three spheres to become ecosystem anchors. Through these sphere-spanning efforts, ecosystem anchors help to shape the evolution of open data initiatives. This research provides a theoretically grounded explanation of processes within successful open data initiatives and suggests new directions for practice.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".