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Record W4362529663 · doi:10.5539/sar.v12n1p61

Banana Farm Workers’ Preference of an Access to Health Care and Education: A Conjoint Analysis

2023· article· en· W4362529663 on OpenAlexvenueno aff
Francis Evan L. Manayan

Bibliographic record

VenueSustainable Agriculture Research · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsConjoint analysisCertificationPreferenceMarketingBusinessHealth careEconomicsEconomic growthManagement

Abstract

fetched live from OpenAlex

In lieu of a concrete living wage calculation for Kapatagan Banana Growers Cooperative (A Cavendish banana grower in Barangay Kapatagan, Digos City) in their conformance to the Rainforest Alliance Standard certification program, this study determined the farm workers’ preference of an access to health care and education program. Conjoint analysis was used in determining the highly preferable attributes among 259 respondents. Through literature and farm records review, the four attributes are formulated: (1) frequency of medical mission, (2) health awareness topics, (3) provision of annual school supplies, and (4) educational information drives. The results revealed that the educational information drive has the highest utility value in which respondents prefer topics on climate change, carbon footprints reduction and waste management. The second variable with the highest utility value is the frequency of medical mission wherein respondents prefer to have it in a semi-annual basis. School supplies came out to be the third in rank (provision of writing materials and books) and the attribute on health awareness value (topics on AIDS/HIV, Tuberculosis and Hepatitis prevention) came out with the lowest utility. Based on the result, the study recommends that the issue on living wage shall not be taken by cooperatives or employers into something that is within the liability concept but rather a responsibility which involves continuous communication and immersion to come up with more appropriate services’ attributes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0000.000
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 head0.053
GPT teacher head0.423
Teacher spread0.370 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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