Police’s Voice: A Need Analysis of ESP for Police Trainees in Malaysia
Bibliographic record
Abstract
The research was to investigate the needs of the former police trainees at a local police training centre (PULAPOL) in Kuala Lumpur for the English course under the Basic Police Training Program (PLAK) in helping their policing tasks on the ground. This was a mixed-method study, employing both needs analysis survey and semi-structured interview as the instruments and were developed based on Hutchinson and Waters (1987)’s Target Needs focusing on Lacks, Wants and Necessities. This study involved 183 former police trainees who used to undergo police training at PULAPOL Kuala Lumpur before; Cadet Police Inspector (CP1) series one (1) and series two (2) 2019 who are now serving as the Inspector Officers (IOs) at various Royal Malaysia Police (RMP) departments nationwide. There were three main findings in this study; firstly, the former police trainees’ Necessities for the English course at PULAPOL were to perform their policing tasks on the ground and to speak with English-speaking clients when solving their problems. Secondly, their Lacks of knowledge in police terminology, grammar and speaking confidence limited their performance in the English course. In terms of Wants, they wished to learn all the English skills equally, but rejected the grammar teaching per say. The outcome of this study will aid the English coordinators at PULAPOL in revising the existing English course and developing a police-based English syllabus or English for Police Purposes (EPP) in accordance with the target needs of the police trainees.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".