Evolving into curriculum makers: the pivotal role of geography teachers as “boundary teachers”
Bibliographic record
Abstract
This study explores how collaborative action research (CAR) can enhance high school geography teachers’ curriculum making capabilities and strengthen their status as “boundary workers.” The research team consisted of a graduate student pre-service geography teacher, a university theoretical mentor, and a high school practical mentor. The study lasted four months and employed a mixed-methods approach, utilizing four rounds of CAR to continuously reflect on and evaluate the effectiveness of boundary work-based high school geography curriculum making and its impact on enhancing pre-service teachers’ curriculum competency. Each CAR round focused on different aspects of curriculum making, such as designing problem situations, strengthening teacher-student interactions, creating localized curricula, and using interdisciplinary themes. The four CAR rounds collectively formed a “boundary work-based geography curriculum making model.” The research findings indicate that through sustained practice of this model, geography teachers, especially those pursuing graduate degrees, can effectively improve their curriculum leadership and become competent “boundary teachers.”
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".