Beyond Transactional Democracy: A Study of Civic Tech in Canada
Bibliographic record
Abstract
Technologies are increasingly enrolled in projects to involve civilians in the work of policy-making, often under the label of 'civic technology'. But conventional forms of participation through transactions such as voting provide limited opportunities for engagement. In response, some civic tech groups organize around issues of shared concern to explore new forms of democratic technologies. How does their work affect the relationship between publics and public servants? This paper explores how a Civic Tech Toronto creates a platform for civic engagement through the maintenance of an autonomous community for civic engagement and participation that is casual, social, nonpartisan, experimental, and flexible. Based on two years of action research, including community organizing, interviews, and observations, this paper shows how this grassroots civic tech group creates a civic platform that places a diverse range of participants in contact with the work of public servants, helping to build capacities and relationships that prepare both publics and public servants for the work of participatory democracy. The case shows that understanding civic tech requires a lens beyond the mere analysis or production of technical artifacts. As a practice for making technologies that is social and participatory, civic tech creates alternative modes of technology development and opportunities for experimentation and learning, and it can reconfigure the roles of democratic participants.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.072 | 0.014 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".