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Record W2595913950 · doi:10.3968/9150

Western Television Programmes and Its Influence on the Cultural Values of Students’ in Taraba State University, Jalingo, Nigeria

2017· article· en· W2595913950 on OpenAlexvenueno aff
Oberiri Destiny Apuke, Kwase Audu Dogari

Bibliographic record

VenueCanadian social science · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Influence and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousSample (material)SociologyConstructivePopulationState (computer science)Work (physics)Survey methodologyData collectionEmpirical researchSocial sciencePsychologyEngineeringDemographyComputer scienceStatistics

Abstract

fetched live from OpenAlex

The study explores the situations surrounding the relationship between Western Television Programmes and the cultural values of the Nigerian youth population, with particular reference to Taraba State University where constructive generalizations were made. Findings were backed-up with an empirical research on 145 respondents from the study area. Cross-sectional Survey design was adopted for this work. The bottle spinning sample technique was used alongside availability/convenient sampling technique. The study also made used of the questionnaire as a means of quantitative data collection. The SPSS (Statistical Packages for Social Sciences) was employed for data analysis and subsequently justified using manual procedures. Six (6) research questions were utilized in empirically justifying the work. Finally, the study discovered that Nigerian Youth prefers viewing WTP more to indigenous TV programmes and this exerts great influence on their cultural values. The study recommends that in salvaging the undue influence vented on the cultural values of youth by Western Television Programmes, there is the need for a constant review of the schooling content of the Nigerian education system, such that its culture would be lucidly pronounced.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.999

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 studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.036
GPT teacher head0.352
Teacher spread0.316 · 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 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

Citations6
Published2017
Admission routes1
Has abstractyes

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