Why Workers Disengage? Factors from “Head” or “Heart” to Be Tagged on?
Bibliographic record
Abstract
Oil palm plantations are decisively situated in the various regions of Malaysia where the climatic conditions are appropriate for planting oil palm. One cannot compare the work environment of such plantations with normal office settings. The workers in those plantations are working in remote locations, totally cut off from the other part of the world. Majority workers are from Indonesia, Philippines like low income countries. The work environment is physically demanding and challenging to the workers due to many factors. An exploratory study has conducted on employee disengagement in the oil palm plantations in the Sabah region of Malaysia. The study has taken up employee disengagement factors due to the lack of interest among the local people to engage themselves in the oil palm plantations work and the plantation industry in Malaysia is facing an acute labor shortage to carry forwards the oil palm business. The study follows, field visits, interviews with the workers, focus group discussions and specifically Delphi. The findings supported to identify the 7 factors coming under the theme employee disengagement as Wage and Welfare, Work Environment, Lack of Safety and security, Poor Organizational Support, Rigid Rules and regulations, and Lack of individual motivation. The study paves better insight to lead this qualitative research in an organized quantitative research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".