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Record W2778211403 · doi:10.5430/ijba.v9n1p28

The Socio-economic Factors Affecting Plant Home Gardens

2017· article· en· W2778211403 on OpenAlexvenueno aff
Jawad Atef Al-Dala’een

Bibliographic record

VenueInternational Journal of Business Administration · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsConsumption (sociology)Family incomeSocioeconomicsGeographySample (material)Distribution (mathematics)Household incomePopulationStratified samplingEntertainmentProduction (economics)Low incomeSocioeconomic statusAgricultural economicsEconomic growthDemographyEconomicsSociologyMedicineSocial scienceArchaeology

Abstract

fetched live from OpenAlex

The objective of this research is to highlight the socio-economic characteristics of households that practice urban plant production through their household gardens. The questionnaire was a tool used to collect data. Stratified sample was which divided the population into six strata. The first five strata were depending on family income, while the sixth strata was depending on the households in suburban areas. The results showed that the distribution of gardens was affected by the family income, the free space inside household. Most of households showed that the production is used either for family consumption or used as entertainment tool inside household. The educational level affected the care for household gardens. In low income families, the low educational individual used to care for gardens, while the contrary was recorded for higher income layers. In the suburban areas, the care for garden was taken over by all family members.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.006
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.249
Teacher spread0.228 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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