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Record W4396818571 · doi:10.63067/xjp2x356

Audience Experience to Series Ertugral and Fulfillment of Social and Psychological Needs Gratification

2023· article· en· W4396818571 on OpenAlexaff
Zarafshan Ansari, Parshant Singh

Bibliographic record

VenueJournalism, politics and society. · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsHumber Polytechnic
Fundersnot available
KeywordsGratificationPsychologySocial psychologyDelay of gratificationSeries (stratigraphy)

Abstract

fetched live from OpenAlex

The study focuses on Audiences’ Exposure to Series Ertuğrul and fulfillment of social and psychological Needs Gratification; moreover, the article thesis calls attention to the age group and gender of youth which is comparatively more fulfilling their social and psychological needs. The age has been grouped under 20 years of age and above. The study explores the needs fulfilled by audience by watching the drama. Frequency of the most Popular Turkish dramas viewership has also been found. Quantitative Survey research method has been used in which total of 251 Responses are collected from National university of Modern languages, Bahria University Islamabad Campus, National University of Science and Technology Quaid e Azam University and International Islamic University via hand-to-hand questionnaires and Online Surveys. Data has been assembled, entered, and tested on SPSS software. Findings and discussions show that Turkish Drama Ertuğrul is being utilized differently by people with different Age groups, Genders, Universities, preference of viewing and frequency of viewing. According to the findings, the respondents gratify most of the needs most likely. The audiences most likely relate the drama to Islam, and they find it as a source of Spiritual maintenance. The researcher further found out that audiences are more likely to learn about and patience and Strength. The drama overall is a good source of social and psychological needs gratification.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.126
GPT teacher head0.367
Teacher spread0.241 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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