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Record W4412072672 · doi:10.32628/ijsrhss242313

Exploring Gender Differentials in Parents’ Perception of Children’s Television Viewing Habit in the Digital Era

2024· article· en· W4412072672 on OpenAlexaff
Olisaeke Lovelyn Chika, Olisaeke Festus Ife

Bibliographic record

VenueInternational Journal of Scientific Research in Humanities and Social Sciences · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Methods and Impacts
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsHabitPerceptionPsychologyDigital eraDevelopmental psychologySocial psychologyComputer scienceThe InternetWorld Wide Web

Abstract

fetched live from OpenAlex

Television viewing is rapidly becoming a habit among individuals, especially children. The concerns about the effects of the habit on children informed this study. The study explores the gender differentials in parents’ perception of television viewing habit among children in the digital era. Using un/structured questionnaire, primary data were sourced from 300 parents in Pankshin, Plateau State, North-Central Nigeria. Survey design, mixed method, and content, thematic, descriptive and statistical analyses along with their allied analytical tools were employed. The results show that gender may influence parents’ perception of and attitude towards children’s television viewing habit, though the influence is insignificant. The pragmatic strategies identified for effective parental control of the habit are affirmed by majority of the respondents. The study concludes gender may influence parents’ perception of and attitude towards the habit, but it is not a determinant of their perception of and attitude towards the habit. Co-operation, collaboration, mutual understanding, and gender re/orientation are some of the recommendations made.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.629
GPT teacher head0.519
Teacher spread0.109 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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