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Record W3026840951 · doi:10.5539/mas.v14n6p64

Governmental Measures towards the Coronavirus Crisis Management: An Applied Study from the Viewpoint of Faculty Members in Jordanian Universities

2020· article· en· W3026840951 on OpenAlexvenueno aff
Abdalhaleem Manaa Aladwan

Bibliographic record

VenueModern Applied Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Crisis managementCoronavirus disease 2019 (COVID-19)PandemicPopulationSample (material)PoliticsPolitical sciencePublic relationsUnit (ring theory)BusinessPsychologyEconomic growthMedicineEnvironmental healthEconomicsDiseaseLawMathematics education

Abstract

fetched live from OpenAlex

This study is considered one of the rare studios in the world that examine the effect of government measures towards managing the Coronavirus crisis, according to the researcher's knowledge, the study relied on the descriptive-analytical approach, as well as on interviews with those in charge of crisis management in Jordan, (254) questionnaires were obtained from the study population sample, the Sampling unit was represented by faculty members in the departments of political science, media, and economics in Jordanian universities. The study reached the following: the high level of government measure implementation in the face of the Corona crisis, that the level of corona crisis management was moderate, and the effect of governmental measures on the management of the Corona crisis is an applied study from the viewpoint of faculty members in Jordanian universities. The study recommended the development of comprehensive visual and written media programs to educate citizens about the risk of disease, intensify health measures through preventive tracking teams for early detection of cases, that security measures not be at the expense of public freedoms, and find solutions to current and expected economic problems due to the crisis.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.098
GPT teacher head0.334
Teacher spread0.236 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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