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Record W2960889579 · doi:10.5430/rwe.v10n2p1

Measuring the Remode Entreprenurship Training Programme at Behaviour Level of Kirkpatrick Model in Aquaculture Industry

2019· article· en· W2960889579 on OpenAlexvenueno aff
Anis Amira Ab Rahman, Mohd Rafi Yaacob, Mohd Asrul Hery Ibrahim, Nor Shuhada Ahmad Shaupi, P.Yukhamarani A P Permarupan, Azlinda Shazneem Md Shuaib

Bibliographic record

VenueResearch in World Economy · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
FundersMinistry of Higher Education, Malaysia
KeywordsEntrepreneurshipFocus groupThematic analysisMarketingDiversification (marketing strategy)PovertyTraining (meteorology)BusinessKnowledge managementPsychologyQualitative researchEconomicsEconomic growthSociologyGeographySocial scienceComputer science

Abstract

fetched live from OpenAlex

The objective of this paper is to reveal the findings which regard to the behaviour level of the Bottom 40 that involved in REMODE training programme. Bottom 40 is a group of household income that lower than RM3,900 per month. The aim of REMODE training programme is to increase the entrepreneurship knowledge that relates to aquaculture industry for poverty eradication. Previous studies on entrepreneurship training programme indicate that utilisation of Kirkpatrick model to measure the impact at behavioural level is still scarce. Most of the study only focuses on the reaction and learning Level. Therefore, this study measured the behavioural level of Bottom 40 who involved in REMODE training programme in order to evaluate the effectiveness of entrepreneurship training programme to fill in the empirical gaps. A qualitative approach using Focus Group Discussion was used to gather data from five groups that consist of twenty three participants. The findings from narrative thematic analysis show that the participants experienced business growth, business diversification, product innovation and business records management due to the knowledge application. This study contributes to the entrepreneurship body of knowledge from the entrepreneurship training perspective. Future research should explore the different type of entrepreneurship training programme measurement and also application to the different type of participants.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.375
GPT teacher head0.347
Teacher spread0.028 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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