水産分野におけるPBLを用いた社会人教育の実践 －PBLの展開過程と応用の可能性に注目して－
Bibliographic record
Abstract
Here the development process and application of Problem-Based Learning (PBL) are shown. Nagasaki Un iversity u ses PBL f or the r ecurren t education . This i s the on ly a nd fron t-en d applied example of PBL in the field of fisheries and its related industry (FFRI). Following points are clarified. 1, Rearranging of the process that PBL widened the applied range; 2, Actual situation of the practice activity using PBL in FFRI, through them; 3, Validity and the cut-end such as aspect of the application technique of PBL in FFRI. PBL appeared in the medical education in Canada in the 1960s. The medical education course propelled improvement of PBL afterward. It was introduced into Japan in 1990, and the engineering system education began that it had been applied afterwards. In PBL, only and independent solution problem is usually set. Group learning by PBL makes the effect for the students and gives them the ability of knowledge, technique, team communication. We now can see on ly a 4 -year practice example for the application to FFRI recen tly. Here, the Tailor-made solution (TMS) such as the one-to-one type PBL education is performed. The framework of the curriculum i s on t he con cept o f cybern etics. B y the past t echn ical s ystem education , the characteristic of PBL is the single problem setting. It was suitable for the training of the ability to solve a typical problem. In contrast, TMS is held because PBL in FFRI expects the solution of practical problem. This TMS is the f irst applied form i n the history o f PBL. The recurrent education in the primary industry will have to analyze/know this example.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.022 |
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".