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Record W3152879286 · doi:10.5539/jel.v10n3p48

Development of a Training Manual for Reducing Use of Pesticides by Para Rubber Farmers at Bueng Khan Province, Thailand

2021· article· en· W3152879286 on OpenAlexvenueno aff
Tivapron Kombusadee, Jurairat Kurukodt

Bibliographic record

VenueJournal of Education and Learning · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsnot available
Fundersnot available
KeywordsTest (biology)Training manualPesticideTraining (meteorology)Medical educationOperations managementToxicologyPsychologyAgricultural scienceBusinessMedicineEngineeringGeographyEnvironmental sciencePolitical science

Abstract

fetched live from OpenAlex

It is widely known that systematically developed training manuals can be used to improve knowledge and practical skills and promote positive attitude of trainees. This research thus aimed to develop a training manual to help reduce the use of pesticides by para rubber farmers in Nonkheng Sub-district, So Phisai District, Bueng Khan Province, Thailand. The research was divided in to 3 stages. The first stage investigated the scenarios in which pesticides were used of the para rubber famers while the second stage dealt with the development of a training manual for promoting the reduction of the use of pesticides and hazardous chemicals by the farmers. The final stage was the evaluation for the efficiency of the developed training manual by applying it with 48 volunteered para rubber famers for 2 days. The findings revealed that the training manual had an efficiency of index at 80.38/80.89. After training with the manual, the farmers’ levels of knowledge increased tal 67.40%, while the overall post-test scores on knowledge, attitude, and practice skills in reducing the use of pesticides were found to be significantly higher (p < 0.05) than those of the pre-test. It can be concluded that the developed training manual can be effectively used to improve the knowledge, attitude, and practical skills of the trainees.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.123

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.285
Teacher spread0.232 · 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 designBench or experimental
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

Citations2
Published2021
Admission routes1
Has abstractyes

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