Identifying, Categorizing and Setting Variables on Ergonomics Issues in Oil Palm Plantations
Bibliographic record
Abstract
It is an eye opening scenario that Malaysia turned to be one of the major producer and exporter of palm oil. Malaysia has witnessed a dazzling growth of 10.06% of its production recently from 4.05 million hectares in 2005 in an area of 54,000 hectares. Further, the production has enlarged from 94,000 tons in 1960 to 15 million tons in 2005, or by almost 160 times within 45 years-this represents a compound annual growth of 11.93% per year. The oil palm industry is labor intensive, since there is less adoption of mechanization in the field operations. Production of agriculture is usually associated with high incidence of ergonomic injuries, mainly during rigorous manual labor and throughout harvesting. Although scientific explanations are available, very less research was conducted to identify and fix variables that are closely related to ergonomics issues of workers in oil palm plantations related to workers absconding and disengagement from work. Field visits were conducted in the present study, to get an insight into the causal factors of ergonomics in relation to workers intention to abscond and disengagement in oil palm plantations was further congregated into. Thus an exploratory study was conducted in the oil palm plantations following qualitative research methods like direct interviews, focus group discussions and Delphi technique arrive at factors and categories related to the ergonomic issues of workers in oil palm plantations. The study provides better insight into the ergonomic issues of workers in oil palm plantations in the Sabah region of Malaysia.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".