Measuring the Remode Entreprenurship Training Programme at Behaviour Level of Kirkpatrick Model in Aquaculture Industry
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".