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Record W1516642533 · doi:10.22230/cjc.2011v36n1a2313

Metaphors for Democratic Communication Spaces: How Developers of Local Wireless Networks Frame Technology and Urban Space

2011· article· en· W1516642533 on OpenAlexfundvenueaboutno aff
Alison Powell

Bibliographic record

VenueCanadian Journal of Communication · 2011
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFraming (construction)DemocratizationDemocracySociologySpace (punctuation)Public spacePolitical scienceTelecommunicationsPublic relationsComputer scienceArchitectural engineeringPoliticsGeographyEngineering

Abstract

fetched live from OpenAlex

Communications policies, like many other social policies, are founded on an ideal of democracy that connects the development of communication infrastructures with democratic public spheres. This framing is a constructivist endeavour that takes place through language, institution, and infrastructure. Projects that aim to develop these capacities must grapple with the way such new media technologies are integrated into existing contexts or spaces, often using metaphors. This article analyzes how such metaphors are employed in the case of local wireless networking. Referring to empirical research on networks located in Montréal and Fredericton, Canada, the article critiques the narrow approach to democratization of communication spaces inherent in networks of this type. This narrow focus is associated with metaphors used to describe a co-evolution of wireless technology and urban space. The article identifies that the design processes that shape these networks could benefit from a more radical democratization associated with metaphors of recombination of space and technology.

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)0.007
metaresearch head score (Gemma)0.015
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 score0.016
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0100.047
Scholarly communication0.0130.019
Open science0.0020.008
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.026
GPT teacher head0.233
Teacher spread0.207 · 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

Citations14
Published2011
Admission routes3
Has abstractyes

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