Transnational Curriculum Transfer and the Role of Feasibility Assessment
Bibliographic record
Abstract
Educational borrowing between and among developed as well as developing nations has a long tradition, much of it not particularly successful. Studies of the introduction of the Inquiry Teaching Method into Korea point to its general failure while assessment of MIT's African Education Programme has shown that the programmes are misunderstood and badly taught. These cases illustrate the difficulty of achieving successful transnational transfer of curriculum. As a Canadian and Tanzanian, we were anxious to overcome such deficiencies when we confronted the task of trying to determine whether an environmental education program developed for high school students in Canada might be readily adapted to the situation in Tanzania
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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 | 0.372 | 0.562 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.026 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".