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Record W2138020766 · doi:10.5539/ass.v10n7p48

Muslim Pilgrims' Use of Electronic Billboards (EBBs) and Message Recall: Examining the Effectiveness of the EBBs as a PSA Tool

2014· article· en· W2138020766 on OpenAlexvenueno aff
Osman Bakur Gazzaz, Fazal Khan, Zafar Iqbal

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicMedia, Religion, Digital Communication
Canadian institutionsnot available
Fundersnot available
KeywordsRecallContingencyOrder (exchange)AdvertisingPsychologyContingency tableSocial psychologyComputer scienceBusinessCognitive psychology

Abstract

fetched live from OpenAlex

The present study has examined the utility of the electronic billboards (EBBs) as a public service announcement (PSA) tool in the Holy Mosque area, Makkah Al-Mukarramah. Data were gathered from a non-probability sample of the Omrah pilgrims on the use, perceptions, and recall of the EBB messages. In-depth interviews with informants were also used to help interpret the data. Pilgrims’ attention to the billboards and their recall of the messages were used as factors of billboard utility and effectiveness. On the basis of frequency distribution analysis coupled with simple elaboration through contingency and partial contingency tables, and zero-order and 5th order partial correlations, the study concludes that despite their potential for high effectiveness the EBBs are not producing much of an impact on the pilgrims. Recommendations are proffered on how best to improve the EBBs utility as a PSA tool for the pilgrims.

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)0.003
metaresearch head score (Gemma)0.020
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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.254
Teacher spread0.224 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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