Bibliographic record
Abstract
The nature of team teaching Team teaching (sometimes called pair teaching) is a process in which two or more teachers share the responsibility for teaching a class. The teachers share responsibility for planning the class or course, for teaching it, and for any follow-up work associated with the class such as evaluation and assessment. It thus involves a cycle of team planning, team teaching, and team follow-up. It allows teachers to cooperate as equals, although when teachers with differing levels of experience share the same class, some elements of a coaching relationship may also occur. We should point out that we do not regard shared teaching as all that team teaching involves. Teachers have sometimes reported to us that by team teaching they mean two teachers planning independently the different parts of a lesson, and while one is teaching the other uses the opportunity to mark homework or take a break! This is not team teaching but simply team planning. The following is an example of two teachers sharing an EFL class in Japan. Vignette A Japanese colleague and I often team-teach some of our classes. We feel that it's a good way for students to experience a different kind of lesson and we both learn from watching how the other teaches the class. We always plan well ahead to make sure we complement each other during the lesson. Sometimes I do the lead in part of an activity and my colleague takes over from me. If there is a group-work activity, of course we are both involved in moving around and facilitating the task.
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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.118 | 0.055 |
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".