Society perception towards security officers in Malaysia: the analysis
Bibliographic record
Abstract
Purpose–The purpose of the present study is to analyse the society perception towards security officers in Malaysia. Society perception towards security officers are based on five dimensions; First Impression on Security Officer, Security Officer Job, Professionalism and Integrity, Satisfaction with Security Officers and Image of Security Officers. \n \nMethodology–Data were collected from 98 random participants among Malaysian and survey was distributed via social media such as WhatsApp, LinkedIn and Facebook. IBM SPSS Statistics 24 was employed to analyse the collected data. \n \nFindings–The results show that the society perception towards security officers are generally neutral with slight satisfaction towards security officers’ service delivery. There is also a significant difference between genders and education level groups in perceiving security officers. \n \nPractical implications–This study can serves as market analysis for the security industry players and policy makers. Society perception towards security officers can be treated as view from potential clients and job searchers perspective. Policy makers and security industry players might use this study to enhance the service quality of security officers and thus, elevate the profession to be more attractive to job market. \n \nOriginality/Value–In spite of quite a number of existing researches (UK, Netherlands, Portugal, South Korea, South Africa, India and Canada) conducted on society perception towards security officers, there is none has been done in Malaysia. The study in Malaysia will serve as initiator for similar studies conducted in South East Asia. \n \nMethodology–Data were collected from 98 random participants among Malaysian and survey was distributed via social media such as WhatsApp, LinkedIn and Facebook. IBM SPSS Statistics 24 was employed to analyse the collected data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".