COMPETENCY IMPROVEMENT OF POLIMARIN LECTURERS BASED ON INFORMATION SYSTEMS THROUGH RETOOLING PROGRAM IN MARINE INSTITUTE CANADA
Bibliographic record
Abstract
The quality and competence of vocational higher education lecturers need to be improved to improve the quality of vocational student education (Mouzakitis, 2010). In implementing higher education, Polimarin has an obligation to develop science so that it can have value benefits for the community. The development of maritime science is among others by improving the quality of lecturers as educators who produce superior human resources. One of the lecturers' competencies that need to be improved is an information system-based maritime lecturer (Pazara, Arsenie, & Pazara, 2010). The shipping security system can be collaborated with information systems that are currently developing very rapidly. So that the application of information systems based maritime security systems can be improved. The implementation of the above program is the implementation of Polimarin's lecturer competency training program through a lecturer retooling program at the Marine Institute Canada. This program can support the government's Nawacita program with the Sea Toll program. So that Polimarin can improve the competency of graduates or human resources in the field of maritime security (Feldt, Roell, & Thiele, 2013) to be more competitive and have superior competitiveness at national and international levels. The output of this training program is that it can develop the science of security systems that are collaborated with scientific information systems (Peslak, 2011). Furthermore, it was stated in the preparation of the Certification Scheme and Competency Test Material which will be held at the Polimarin Professional Certification Institute. Article visualizations:
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".