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Record W2099521390 · doi:10.15353/joci.v3i2.2380

Factors Influencing Information Delivery Technology Choice in Deprived Regions in Ghana

2007· article· en· W2099521390 on OpenAlexvenueno aff
Olivia Adwoa Tiwaah Frimpong Kwapong

Bibliographic record

VenueThe Journal of Community Informatics · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsWillingness to payContingent valuationProxy (statistics)BusinessInformation and Communications TechnologyContext (archaeology)MarketingRural areaEconomic growthPublic economicsEconomicsGeographyPolitical science

Abstract

fetched live from OpenAlex

Using a contingent valuation (CV) method to quantitatively estimate the influence of selected socio-economic factors on households’ willingness to pay for alternative information delivery technologies, the study intended to provide basic information regarding rural households’ willingness to pay for information delivery technologies. This study used rural household survey data collected from three administrative regions in Ghana to examine rural women’s willingness to pay for information delivered via three technologies – community radio, private radio, and extension agents. The primary objective of the study was to identify the critical factors to consider in planning and policy design in using ICT to provide information to empower rural women. While there were nontrivial regional variations, the overall results from this study point to household expenditures (used as proxy for income), household education, and membership in community organizations as the principal factors influencing rural women’s willingness to pay for the various technologies used in information delivery to women in rural areas in Ghana. The overriding conclusion that emerged from this study was the need to examine ICT use in empowering rural women within a ‘holistic’ context.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.056
GPT teacher head0.250
Teacher spread0.194 · 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 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

Citations2
Published2007
Admission routes1
Has abstractyes

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