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Record W1497152477 · doi:10.15353/joci.v1i1.2064

Local Learnings: An Essay on Designing to Facilitate Effective Use of ICT s

2005· article· en· W1497152477 on OpenAlexvenueno aff
Tony Salvador, John F. Sherry

Bibliographic record

VenueThe Journal of Community Informatics · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyConversationEthnographyICTSWork (physics)SociologyDigital dividePublic relationsFrame (networking)BusinessKnowledge managementPolitical scienceComputer scienceTelecommunicationsEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

In this essay, we explore some of the details of what it takes to own, use and derive benefit from information and communication technologies, with a focus on regions where ICT adoption and use is especially low. We begin with a fairly meticulous description from our ethnographic work to which we'll refer throughout the paper. Though we consider this particular instance, we note that it represents of a wide range of instances from our ethnographic work in homes and businesses over several years in Brazil, Costa Rica, Chile, Ecuador, Bolivia, Peru, Korea and India. Our goal in this paper, however, is to change the conversation from discussions of infrastructure and capacity building to considerations of local, lived conditions in actual homes and actual businesses to suggest design alternatives that make effective use of ICTs more amenable to various locales. We offer two design directions especially for high tech corporations: Designing for Locus of Control and Designing for Local Participation. Along the way, we'll argue to re-frame of the current conception of "digital divide", putting the burden not on those with limited access, but on limited understanding within the high tech industry.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.262

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 studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.063
GPT teacher head0.262
Teacher spread0.199 · 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

Citations9
Published2005
Admission routes1
Has abstractyes

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