Civic Hackathons: Innovation, Procurement, or Civic Engagement?
Bibliographic record
Abstract
Abstract At all levels, governments around the world are moving toward the provision of open data, that is, the direct provision to citizens, the private sector, and other third parties, of raw government datasets, controlled by a relatively permissible license. In tandem with this distribution of open data is the promotion of civic hackathons, or “app contests” by government. The civic hackathon is designed to offer prize money to developers as a way to spur innovative use of open data, more specifically the creation of commercial software applications that deliver services to citizens. Within this context, we propose that the civic hackathon has the potential to act in multiple ways, possibly as a backdoor to the traditional government procurement process, and as a form of civic engagement. We move beyond much of the hype of civic hackathons, critically framing an approach to understanding civic hackathons through these two lenses. Key questions for future research emphasize the emerging, and important, nature of this research path.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".