Cross-cultural Reciprocal Learning: A Seven Year Partnership Between Canada and China
Bibliographic record
Abstract
This symposium provides an opportunity to share the findings, challenges and interactions of researchers, graduate students and participants focusing on a reciprocal learning project between Canada and China. This SSHRC-supported research was designed to build a knowledge base about reciprocal learning in a number of areas of education. This symposium will focus on the mathematics team with four sister schools in Canada and China and how what we learned from the interactions of teachers, administrators, researchers and graduate students. Overall, the teachers were motivated to learn about each other and the cultural differences in the way mathematics is viewed and taught in the two countries. Problem solving was a major focus of one pair of sister schools while individual teacher pairs and their emerging questions were the focus of other teachers and administrators. Two papers will be presented followed by a panel discussion based on three key questions and questions posed by the audience. A well-known mathematics researcher will be the discussant.
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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.051 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".