Bibliographic record
Abstract
Data governance for data sharing is becoming an important issue in the rapidly evolving data economy and society. In the smart cities’ context, data sharing may be particularly important, but is also complicated by a diverse array of interests in data collected, as well as significant privacy and public interest considerations. This paper examines the data governance body proposed by Sidewalk Labs as part of its Master Innovation Development Plan for a smart city development on port lands in Toronto, Canada. Using Sidewalk Lab’s Urban Data Trust as a use case, this paper identifies some of the challenges in designing an effective and appropriate data governance structure for data sharing, and analyzes the normative issues underlying these challenges. In this example, issues of data ownership and control are contested from the outset. The proposed model also raises interesting issues about the role and relevance of the public sector in managing the public interest; and the need to design data governance from the ground up. While the paper focuses on a particular use case, the goal is to distil useful knowledge about the design and implementation of data governance structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.079 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".