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Record W4313238501 · doi:10.31468/dwr.979

User Experience and Digital Government: Exploring a Practice-Based Participatory Approach to Identify Research Opportunities

2022· article· en· W4313238501 on OpenAlexaffvenueabout
Isabelle Sperano, Robert Andruchow, Luca Petryshyn, Vik Chu

Bibliographic record

VenueDiscourse and Writing/Rédactologie · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsMacEwan University
Fundersnot available
KeywordsTimelineGeneral partnershipParticipatory action researchGovernment (linguistics)Citizen journalismKnowledge managementWork (physics)Public relationsRelation (database)Process (computing)Digital governmentParticipatory designProcess managementPolitical scienceSociologyEngineeringComputer scienceDigital transformationWorld Wide Web

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.539
GPT teacher head0.459
Teacher spread0.080 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes3
Has abstractyes

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