MétaCan
Menu
Back to cohort
Record W2790430732 · doi:10.5539/mas.v12n4p57

Begging Phenomenon in Jordan: Family Role and Causes

2018· article· en· W2790430732 on OpenAlexvenueno aff
Taghreed Salsi Ali Al-Muhareb, Mohammad Sayel Alzyoud

Bibliographic record

VenueModern Applied Science · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsBeggingUnemploymentPhenomenonDemographic economicsSample (material)PopulationSocial capitalPsychologySociologySocial psychologyEconomic growthEconomicsDemographyPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

The study aimed to find out the role of the family in facing begging phenomena and its causes from the point of view of Jordan Families. The study population consisted of 865339 families from Amman, the Capital of Jordan. The study sample was chosen randomly, and it consisted of 4750 families. The study used a questionnaire to collect its data. To answer the first and third questions, means and standard deviations for each item and the whole domain were calculated. To answer the second question, the means, standard deviations, t-test, and one way ANOVA Analysis were used. The study revealed that some families encourage their kids to beg and seek help from others. In addition, families are busy with other responsibilities and they do not give their kids the required support that educate them and keep them away from begging. Also, there are multiple reasons that have stood behind the wide spread of the begging phenomenon such as the current difficult situation that Jordan society experience due to economic, social, and political conditions. The study revealed that low level of education for mothers is not behind the begging phenomena, but rather it is the family income at first. Unemployment reduces family income which makes the individuals to search for other means of satisfying the family needs. This, however, finally result to begging.

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)0.001
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 score0.381
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.046
GPT teacher head0.381
Teacher spread0.335 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicHomelessness and Social IssuesFrench-language works237,207