Constructing a Public Narrative of Regulations for Big Data and Analytics: Results From a Community-Driven Discussion
Bibliographic record
Abstract
This article reports on community perspectives about the regulation of municipality-led Big Data initiatives developed through an exploratory, deliberative democracy-informed approach. While analytics hold great promise for policy design and service delivery improvements, their mythologized nature may elicit a blind faith in empirical outcomes, leading to misrepresentation or omission of marginalized populations. Scholars have begun pointing to public consultation as a means of avoiding these challenges, suggesting that a truly “smart city” should vet potential Big Data polices through the community in order to identify locally relevant concerns. The Big Data in Cities: Barriers and Benefits symposium, held in May of 2017, took a deliberative democracy approach designed to contribute toward a midsized southern Ontario city’s regulatory framework for data aggregation and mobilization. Approximately 100 self-selected participants (primarily public advocates) attended a 2-day symposium that featured a series of presentations designed to introduce critiques to and strategies for the implementation of Big Data initiatives. Participants also engaged in several facilitated roundtable discussions during the symposium, and their transcribed conversations served as the data for this study. Thematic analysis identified three recurrent concerns: publicly vetted data ethics, consultation and literacy practices, and regulatory frameworks. The public consultation process employed by this study produced results that reflect critiques raised in other academic papers.
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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 | 0.189 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.050 | 0.050 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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".