MétaCan
Menu
Back to cohort
Record W4248957825 · doi:10.33915/etd.2648

Identifying critical factors for developing effective rural community technology centers

2005· dissertation· en· W4248957825 on OpenAlexaff
Daphne Gooding

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsDillon Consulting
FundersU.S. Department of Housing and Urban Development
KeywordsRural communityThematic analysisProtocol (science)InformaticsQualitative researchMedical educationPublic relationsPsychologyKnowledge managementMedicinePolitical scienceComputer scienceSociologySocioeconomicsSocial scienceAlternative medicine

Abstract

fetched live from OpenAlex

The purpose of this research is to inform both existing and developing community technology initiatives as to the critical factors for building effective rural community technology centers. Rural community technology centers which had been operating for at least two years were identified and contacted by telephone. Either a paid or volunteer staff person was interviewed using a semi-structured protocol of open-ended questions. Responses were taped, transcribed and coded using standard tools and procedures for qualitative investigation. Codes were grouped in 12 thematic groups. Relative occurrences of codes within each group were analyzed. Participants were asked what criteria were used to measure effectiveness of their centers. Participants also made recommendations about alternative evaluation metrics that could be evidence of the impact of their centers on participants. The findings suggest eleven areas that require attention when developing rural community technology centers or networks. Results also support Maughan's model of a robust communication system and Kling's Social Informatics theory.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0010.001
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 head0.040
GPT teacher head0.350
Teacher spread0.309 · 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.

Study designTheoretical or conceptual
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

Citations1
Published2005
Admission routes1
Has abstractyes

Explore more

Same topicICT in Developing CommunitiesFrench-language works237,207