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Record W2750946399 · doi:10.5539/jel.v7n1p42

Information Technology and Transcription of Reading Materials for the Visually Impaired Persons in Nigeria

2017· article· en· W2750946399 on OpenAlexvenueno aff
Christopher Nkiko, Morayo Ibironke Atinmo, Happiness Chijioke Michael-Onuoha, Julie E. Ilogho, Michael Fagbohun, Ifeakachuku Osinulu, Basiru Adetomiwa, Kazeem Omeiza Usman

Bibliographic record

VenueJournal of Education and Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsTranscription (linguistics)Descriptive statisticsInformation technologyDescriptive researchPsychologyMedical educationApplied psychologyComputer scienceMedicineSocial scienceStatisticsSociologyMathematics

Abstract

fetched live from OpenAlex

Studies have shown inadequate reading materials for the visually impaired in Nigeria. Information technology has greatly advanced the provision of information to the visually impaired in other industrialized climes. This study investigated the extent of application of information technology to the transcription of reading materials for the visually impaired in Nigeria. The study adopted survey research design of the ex-post facto to select 470 personnel as respondents. A questionnaire titled Information Technology Use Scale (α=0.74), and Interview Schedule (α=0.75), were used. Data were analyzed using descriptive statistics and Pearson Product Moment Correlation. The findings indicate that information technology in transcription was low and a significant positive relationship between application of information technology and transcription of information materials (r=0.62: p<0.05). The study recommended among others that Multi-National Corporations should be sensitized to extend their Corporate Social Responsibility (CSR) activities to help in procuring modern information technology devices and software to enhance transcription.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.332
Teacher spread0.304 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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