Bibliographic record
Abstract
This thesis examines intersections of civic participation and technology development in Toronto, Canada. In particular, I examine processes that configure civic participation by focussing on the way that people enrol and are enrolled by technology oriented toward the pursuit of public goods, a practice known by those involved as ‘civic tech’. Using ethnographic and action research methods, I examine processes of design and participation at multiple sites, characterised by differing levels of state and civilian involvement. In the first article, I explore the 2018-2020 corporate-run engagementprocess case of the Sidewalk Toronto smart city development, arguing that while certainly robust in scope, the process marginalised participant contributions by maintaining boundaries around key normative dimensions of the proposed smart city, especially around the governance of space, data, and other infrastructures. In the second and third article, I shift focus to Civic Tech Toronto (CTTO), a volunteer-led community group that, since 2015, has gathered civilians and public servants together at weekly hacknights, where they listen to speakers and experiment with technology projects addressing civic issues. The second article examines the governance and production of CTTO, exploring the applicability of commons based peer production language to the community and arguing that its main role as a technology itself is to produce relationships and experiences of community, rather than technological artefacts. Finally, the third article explores how CTTOs activities act as a contact zone that brings civilians and the state closer together in informal relationships. By prioritising social and self-directed cooperation, CTTO moves beyond the typical transactional nature of civic engagement (i.e. one that configures people as ‘users’ of the state or sees them as providers of informational ‘feedback’), and equips both civilians and public servants with the skills they need to interact more productively. Collectively, this research advances our understanding of the value of civic tech not only in terms of the artefacts it produces, but in the infrastructuring work it does: civic tech maintains civic commons that cultivate democratic subjects and creates spaces that ultimately help build trusting relationships between publics and public servants.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.025 | 0.021 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".