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Bibliographic record
Abstract
This paper attempts to describe the actual conditions of institutional and substantial teaching-stream faculty (teaching-centered faculty members) in Canada and several advanced countries, and consider their backgrounds, characteristics and issues. Not only in Canada but also in some countries the system of teaching-stream faculty including a full-time faculty appointment and opportunities to promote to a professor has already been established, and their contributions have been highly evaluated by their colleagues while they have been so much satisfied with their positions. In some other countries the national policy relating to teaching-stream faculty has not been introduced yet, but the balance of teaching and research is separately adjusted according to an individual and a university. In the background they are similarly facing the drastic changes in higher education such as an increase in diverse enrollment and teaching burdens, a relative decrease in public research funds, more emphasis of research results, and gradual expansion of accountability of the teaching effects. Though those actions may raise a serious concern about resulting in the development of a two-tiered faculty environment and destruction of the traditional balance of teaching and research, a new concept of faculty and university must be pursued towards these inevitable shifts, and at the same time, especially in Japan, individual faculty evaluation should be revised as quickly as possible so that it may reflect on scholarship of teaching and learning as academic work.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".