Development of an English Writing Model: A Guide to Self-Directed Learning for Local Food Product Entrepreneur
Bibliographic record
Abstract
This study aimed to analyse local food product labelling and information and to develop a writing model focusing on local food product labelling and information. Ten entrepreneurs in one province in the south of Thailand selling local food products were selected. The research employed the quantitative research approach which involved three phases of data collection. In phase one, a questionnaire was employed to gain data necessary for the development of a writing model and to study local food products and labelling of 10 local food products in the selected province. Phase two involved the development of a writing model in which document analysis was employed to strengthen the content included in the writing model. In phase three, a satisfaction questionnaire was utilized as an additional data to adjust the writing model and confirm its usage. The study revealed that the participating entrepreneurs placed an importance on English food labelling. Consequently, the writing model was developed to meet the needs of the participants. The components of the writing model included four major units of information which were product safety, product value, product advertising, and product reliability and warranty. In each unit, crucial information was provided to allow users to study relevant information and related food vocabularies by following the step-by-step information in a form of Thai-English translation. Along with the information provided, the writing model presented a process of writing which can enable the users to develop their product labelling in English.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".