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Record W1593231985 · doi:10.15353/joci.v3i4.2356

Technology Mediated Learning: Building Capacity in Rural Communities

2008· article· en· W1593231985 on OpenAlexvenueaboutno aff
David W Bruce, Victoria Hagens, Katrina R. Ellis

Bibliographic record

VenueThe Journal of Community Informatics · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsLearning communityThe InternetRural communityInformal learningPublic relationsSocial contactBusinessValue (mathematics)Social learningCapacity buildingProfessional learning communityKnowledge managementInternet privacyProfessional developmentPsychologyComputer scienceWorld Wide WebSociologyPedagogyEconomic growthPolitical scienceSocioeconomicsSocial psychology

Abstract

fetched live from OpenAlex

Internet access in rural Canada increases opportunities for learning and interaction among community leaders. Research on the use of chat room technology to facilitate learning and development revealed that there were significant barriers, including time constraints, lack of prior social and professional contact among participants, and limited value placed on informal learning relative to other daily activities. Communications technologies such as chat rooms have the potential to help meet personal and community goals, but they must be effectively combined with other community assets and circumstances for the benefits of their use to be realized.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.261
Threshold uncertainty score0.518

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0040.002
Open science0.0020.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.063
GPT teacher head0.246
Teacher spread0.183 · 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 designNot applicable
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

Citations8
Published2008
Admission routes2
Has abstractyes

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