Economic Development Resiliency: Mobilized Disaster’s Readiness Among Higher Learning Students in Malaysia
Bibliographic record
Abstract
Resilient economic development is supported by each and every people as a whole. To deal with disasters, we should not act alone. Readiness is one part of the process of disaster management and natural disasters that disrupt the living system. The study was conducted to measure the level of readiness for emergencies and disasters among youth (eg: high learning students) and to develop items for factors that influence students' ability to serve in disaster areas. To achieve the following objectives, two analyses were conducted: the first analysis was the comparison of mean scores and the second analysis was the factor analysis involving four factors, physical factors, university management factors, financial factors and personal factors. This study uses questionnaire and online form to collect data from respondents. A total of 120 respondents from three higher learning in Malaysia – Universiti Pendidikan Sultan Idris (UPSI), Universiti Sains Malaysia (USM) and University of Technology (UiTM) were involved in this study. The level of readiness has a high mean value and indicates a good level of readiness among the respondents. Factor analysis showed that there were no significant differences between the techniques of determining the number of factors or not for all the items that were formed. Nevertheless, the Cronbach Alpha values indicate that the items are constructed and that the overall research tool has internal consistency values. This study is expected to increase the engagement and spirit of volunteerism as it can add value to the students and even produce a high quality national leadership.
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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 | 0.001 |
| 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) | 0.002 | 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".