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Record W2062246227 · doi:10.1145/2556288.2556970

Posting for community and culture

2014· article· en· W2062246227 on OpenAlexafffund
Claude Fortin, Carman Neustaedter, Kate Hennessy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPersonalizationComputer scienceAgency (philosophy)Flexibility (engineering)Citizen journalismSpace (punctuation)World Wide WebInternet privacyParticipatory cultureParticipatory designRelevance (law)Public spaceMultimediaHuman–computer interactionSociologyEngineeringPolitical science

Abstract

fetched live from OpenAlex

The next decade is likely to see a shift in digital public displays moving from non-interactive to interactive content. This will likely create a need for digital bulletin boards and for a better understanding of how such displays should be designed to encourage community members to interact with them. Our study addresses this by exploring community bulletin boards as a ubiquitous type of participatory non-digital display "in the wild". Our results highlight how they are used for content of local and contextual relevance, and how cultures of participation, personalization, location, the tangible character of architecture, access, control and flexibility might affect community members' level of engagement with them. Our analysis suggests entry points as design considerations intrinsically linked to the users' sense of agency within a delineated space. Overlaps with related work are identified throughout to provide further validation of previous findings in this area of research.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.072
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.003
Scholarly communication0.0080.006
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0720.009

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 head0.018
GPT teacher head0.272
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2014
Admission routes2
Has abstractyes

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