Bibliographic record
Abstract
The Royal Malaysian Air Force (RMAF) has faced difficulties in achieving and sustaining at least 70% of its aircraft availability (Av) in order to support its operational requirements. The head start for this research is to discuss with a focus group (FG) which comprise of eight officers and one moderator and supported by observation on the field. The FG highlighted that the low Av was due to the ineffectiveness and inefficiency of the through life cycle support (TLCS) as a result of weaknesses in the acquisition conceptual framework (ACF). Three research questions were put forward; Q1: Why has the RMAF not achieved its aircraft Av as its desired objectives? Q2: How do the RMAF’s present acquisition practices given a significant impact to Av? And Q3: What is the recommended ACF to be used to ensure higher aircraft Av? The mix mode method (quantitative and qualitative) data collection was used. The literature review focused on critical success factors (CSFs) in terms of acquisition, terms and definition, and present practices in the Royal Malaysian Army (RMA), the Royal Malaysian Navy (RMN), the Malaysian public sector, the Department of Defence of the United States of America (DoD USA), the Ministry of Defence of United Kingdom (MoD UK) and the Australian Defence Force (ADF). Based on the CSFs from the literature review, a preliminary ACF I was developed. The RMAF case study had focused on Type A, Type B, Type C and Type D aircraft. Data on aircraft status for FY 2011 to 2015 was gathered from the Air Support Command Headquarters (ASHQ). The survey was achieved through 16 self-administered structured questionnaires which are close-ended involving 120 out of 150 respondents from the Worker Group (WG). The interviewer collected qualitative data using 21 semi-structured questionnaires with open-ended answers on 20 respondents from the Management Group (MG). The survey and interview results were presented in a matrix table and categorized in accordance with themes and their relationships. Based on the results of the case study, the preliminary ACF I was modified to ACF II. Then, ACF II was validated by four experts who comprise of two senior officers and two senior managers from the aviation industry. After validation, the ACF II was modified to ACF III (final) and was proposed for implementation. Three project objectives were put forward. Objective 1: To identify the cause of low Av.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".