Introduction -- Broadening Teacher Candidates’ Horizons: An Introduction to the Teacher Education Reciprocal Learning Program
Bibliographic record
Abstract
The Teacher Education Reciprocal Learning Program (RLP) is a collaborative initiative between the University of Windsor (UW), Canada, Southwest University (SWU), China, in partnership with Greater Essex County District School Board and Chinese schools associated with SWU. The program, founded in 2010 through SWU Teacher Education fund and UW Strategic Priority Fund with in-kind contributions from Greater Essex County District School Board, is designed to provide an exceptional experience with international engagement, to broaden teacher candidates’ horizons for a society of increasing diversity, to foster international collaboration among faculty members who are interested in cross-cultural studies and multicultural education, and to enhance the international reputation of the University of Windsor (Xu, 2011a). The RLP is one of the foundational programs which provide research contexts and settings for the Social Sciences and Humanities Research Council (SSHRC) Partnership Grant Project entitled “Reciprocal Learning in Teacher Education and School Education between Canada and China” (Xu & Connelly, 2013-2020).
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 | 0.004 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".