[実践報告] 学生の海外派遣事業におけるFD・SDプログラム開発 : 大学間連携の取り組みから
Bibliographic record
Abstract
The first purpose of this article is to report how FD (Faculty Development) and SD (Staff Development) have been implemented in the Student Dispatch Program and examine their effects. The second purpose is to discuss the significance and challenge to implement FD/SD in the project which does not aim FD/SD as a main objective. The Hyogo University Collaboration project is divided into three programs; student dispatch program, student acceptance program and development of international FD/SD program. The dispatch program mainly aims to strengthen students’ English skill through participating in a training course at the US/Canada institutions. Before studying in abroad, the students are required to take pre-training courses such as English skill development, inter cultural understanding, fieldwork preparation and risk management by the specialists or professors engage in the collaboration project. Utilizing these opportunities, in order to develop FD/SD program, goals were set beforehand; FD aims to obtain teaching skills by involving in pre-training courses, design a scheme for US/Canada training course and accompany the students abroad. At the same time, SD aims to acquire the knowledge and skills for official procedures to dispatch students, recognize the necessary content of pre-training courses and adapt the technical-know-how of the dispatch program to their university program. A short survey was taken to see the effects of the program at each FD/SD occasion. As a result, the aims set out first were mostly achieved, for example, the faculty could increase the range of instruction in terms of teaching/researching, design the ideal course in US/Canada based on the objective and apply the teaching method observed abroad for their classes. Moreover, the participants were able to obtain the knowledge of official procedures to dispatch students abroad and risk management system. One of the most effective results were that participants from different universities could make a network through mutual interaction. The future challenge for FD/SD in the collaboration project is to develop courses which small-sized or individual universities can utilize to overcome the difficulties. Furthermore, it is important to anticipate the courses based on the participants’ needs and contrive a stratagem for the program connected to practicaluse.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".