“We Are Scholars”: Using Teamwork and Problem-Based Learning in a Canadian Regional Geography Course \n
Bibliographic record
Abstract
This pedagogical reflection recounts the implementation of a team-based and problem-based \nlearning format in a regional geography of Canada course at a Canadian university. Regional geography \ncourses, popular in many collegiate geography departments, often rely on the “transmission” mode of \nlearning, which relies on the presentation of factual information about regions and its recitation in \nexaminations. This format tends to reify existing regional divisions, whether political or otherwise, and \nmakes it difficult for students to comprehend the dynamic, historical and constructed nature of regions. \nTeam-based and problem-based learning was deployed in this third-year course to enliven and enrich the \nstudy of regional geography through the use of learning groups which produced regular research \nproducts during a series of thematic modules. Based on student feedback and the instructor’s reflections, \nthe article highlights key benefits of teamwork in terms of learning outcomes and student personal \ndevelopment.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".