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Record W2294315392 · doi:10.20380/gi2015.20

Moving towards user-centered government: community information needs and practices of families

2015· article· en· W2294315392 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCanada Human-Computer Communications Society · 2015
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAffordanceLeverage (statistics)Face (sociological concept)Government (linguistics)Public relationsInternet privacyKnowledge managementInformation needsComputer scienceSociologyWorld Wide WebPolitical scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

Government organizations have begun to consider how to provide families with information about their communities, yet their current design strategies focus on providing any and all of their information. This makes it difficult for families to find what is relevant to them. To help address this problem, we conducted a diary and interview study to explore what community information families are actually interested in, how and when they acquire it, and what challenges they face in doing so. Results show that location-based information in their environments triggered people to want to know more about their community while time-based information helped people plan family activities. Family members also wanted to have information resurface at particular places and points in time to support face-to-face interactions. Our analysis suggests design opportunities to leverage the affordances of print and online media and the use of in-home technologies to support the interactions between family members. We also suggest considerations for location-based experiences within communities.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0030.002
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 head0.067
GPT teacher head0.307
Teacher spread0.240 · 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