Your Comments Here: Contextualizing Technologies, Seeking Records and Supporting Transparency for Citizen Engagement
Bibliographic record
Abstract
Today, governments the world over are opening decision-making processes to citizen engagement as an aspect of open government. Citizen engagement initiatives may range from making information available and seeking feedback, to highly dynamic processes that transfer authority to communities and individuals. As part of these initiatives, governments are actively using digital technologies to gather, analyze, and store citizen input; activities that in turn create an array of records. My paper surveys a range of digital technologies used by Canadian citizen engagement case initiatives. In linking technologies, recordkeeping and citizen engagement, I present the combined frameworks of the IAP2 Spectrum and archival diplomatics as one method of understanding how recordkeeping and citizen engagement frameworks may be joined. I conclude with a discussion on defining and locating the records of citizen engagement initiatives and how records and recordkeeping may support transparency and trust in citizen engagement.
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 | 0.012 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".