An Assistive and Research Framework Methodology for Ships’ Upkeep and Repair Organisational Learning Performance
Bibliographic record
Abstract
The Omani Dockyard (OD) requires the development of a research methodology, which encompasses an assistive framework to maintain the research boundary to support a research framework. A research framework is developed to understand the behaviour of variables. A deductive / quantitative – survey questionnaire is employed in the main research to statistically understand the ‘mindsets / opinions’ of a large population and an inductive / qualitative – semi-structured interview using selected senior managers for the total research. Another questionnaire was used to critically learn from the agreement of the senior managers if the proposed contributions were in line with the ships’ upkeep and repair ‘organisational performance’. The initial and most definitive requirement is also to understand the strength of independent and mediation constructs applicability for the enhancement of performance. The problem is in the area of ‘enhancement of organisational learning on knowledge and competencies’ to underpin ships’ upkeep and repair support performance for better availability of operational ships. This research methodology was designed for a ‘major piece of research’ involving a doctorate dissertation in ships’ support performance. The conclusion and recommendation for a ‘major piece of research’ formulated the framework / model to underpin performance. This study concentrates on the research methodology that was used for ships’ upkeep and repair performance of the Omani dockyard with a compressive description of the total results, which can be generalized for other studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.053 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".