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Record W1894052220 · doi:10.15847/obsobs21200881

Making Sense of Broadband in Rural Alberta, Canada

2008· article· en· W1894052220 on OpenAlexaffabout
Maria Bakardjieva

Bibliographic record

VenueObservatorio (OBS*) · 2008
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAppropriationPublic relationsSocial constructivismGovernment (linguistics)SociologyPolitical scienceContext (archaeology)PoliticsThe InternetEconomic growthPublic administrationSocial scienceGeographyEconomicsComputer science

Abstract

fetched live from OpenAlex

This article stems from a collaborative research initiative that examined the social adoption of the SuperNet, an Alberta government infrastructure project designed to provide high-speed, broadband access to public facilities, to businesses and residences in Alberta communities. The aim is to explore how rural community members made sense of the SuperNet as a communication technology in the context of their practices and perceived needs and against the background of their existing experience of Internet use. The theoretical underpinnings of the approach taken in the research derive from social constructivism and critical theory of technology. Members of rural communities in their capacity as current and/or potential users of the SuperNet were construed as relevant actors in the social shaping of the network. In the process of the research it became clear that these activities themselves constituted an important stream in the meaning-making and hence social shaping of the SuperNet. The article addresses the question of what economic, political and cultural influences of a national (and provincial) character may be responsible for the observed developments. It also discusses the specifics of rural appropriation of broadband in Alberta and the conditions and outcomes of the creativity of rural users.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.284

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.005
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.222
Teacher spread0.198 · 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 designObservational
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

Citations6
Published2008
Admission routes2
Has abstractyes

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