User Experience and Digital Government: Exploring a Practice-Based Participatory Approach to Identify Research Opportunities
Bibliographic record
Abstract
In this case study, the research team (RT) explores user experience design in relation to digital practices adopted by governments. The goal of this first phase was to identify research opportunities. To do so, the RT adopted a practice-centered participatory research approach (Holkup, 2004). The RT began a partnership with a municipal government (City of Edmonton). Regular meetings were held with the partner organization to discuss—among other things—the organization’s structure, current and future projects, the digital editorial strategies implemented by the organization, and the organization's issues and constraints when designing digital services. This allowed the teams to identify not only interesting research questions but also potential teaching collaborations related to work-integrated learning. In this paper, the practice-based participatory research approach is explained, the timeline and the outcome of the partnership are presented, and the lessons learned through that process are shared.
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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 | 0.058 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".