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Record W1530263734 · doi:10.15353/joci.v10i2.2644

The First Mile of Broadband Connectivity in Communities: Introduction to the Special Issue

2014· article· en· W1530263734 on OpenAlexaffvenueabout
Rob McMahon, Duncan Philpot, Susan O’Donnell, Brian Beaton, Tim Whiteduck, Kevin D. Burton, Michael Gurstein

Bibliographic record

VenueThe Journal of Community Informatics · 2014
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsAssembly of First NationsUniversity of New Brunswick
Fundersnot available
KeywordsLast mile (transportation)MileTelecommunicationsBroadbandBroadband networksDigital divideBusinessEngineeringThe InternetComputer scienceGeographyWorld Wide Web

Abstract

fetched live from OpenAlex

In this special issue , “First Mile” refers to broadband infrastructure development that puts the needs of local communities first and ahead of the needs of private sector telecommunication corporations. This approach is one that prioritizes community-led solutions that seek to create local economic and social opportunities and to minimize the digital divide between rural and urban users (see: McMahon, O’Donnell, Smith, Walmark, Beaton, & Simmons, 2011). Around the world, broadband infrastructure and networks are rapidly being developed in communities marginalized in the network society. The relationships, structures and agreements put into place at this early development stage will shape how broadband systems are created and managed in the future. First Mile strategies include developing locally owned and managed telecommunication structures and networks. This special issue profiles First Mile projects and efforts that are as innovative, unique and vibrant as the communities from which they emerge. Further, this issue highlights some of the challenges facing First Mile initiatives. Several contributions in this issue deal with Canadian cases and others with remote and rural contexts around the world.

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 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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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 teacher head, 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

Citations3
Published2014
Admission routes3
Has abstractyes

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