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Record W3124728540 · doi:10.5430/rwe.v12n1p279

The Role of Social Marketing in the Prevention of Corona Virus (Covid-19) in Jordan

2021· article· en· W3124728540 on OpenAlexvenueno aff
Khaled Tawfeq Al Assaf, Mahmoud Hudaib

Bibliographic record

VenueResearch in World Economy · 2021
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Social marketingPandemicCoronavirus disease 2019 (COVID-19)Public relationsPolitical scienceGeneral partnershipEconomic growthSample (material)MarketingBusinessSociologyEconomicsMedicine

Abstract

fetched live from OpenAlex

This study aimed to identify the role of social marketing (government role, community culture, reference groups) in the prevention of Coronavirus in Jordan. The study was conducted on a random sample targeting all individuals in Jordanian society from different groups lived in other regions, The outcomes of the study indicated that the variable of governmental role adopts the concept of social responsibility with a high average, and the results of the survey signposted that each of the variables of community culture and reference groups has broad contributions in the field of social marketing; represented in the guidance and educational dimensions in combating the pandemic, and the most significant recommendation proposed by the study is the necessity to intensify the efforts of reference groups and celebrities - especially economists - in raising the level of awareness regarding health and economic risks in such exceptional circumstances, and the need to strengthen the community-partnership between the government and community - companies and individuals - in all aspects regarding social responsibility towards the country, and raise awareness levels of societies, especially in developing countries.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.071
GPT teacher head0.361
Teacher spread0.290 · 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 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
Published2021
Admission routes1
Has abstractyes

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