Bibliographic record
Abstract
This article focuses on the IB-PYP curriculum and teachers’ roles within IB-PYP. The Turkish national curriculum was used to contextualize the paper, and these two curricula have been presented comparatively. The comparison encompasses their respective scoping aims, models of curricular control, distinctive teacher roles, and assessment practices. Moreover, the study incorporates insights and viewpoints from PYP teachers who also had worked for long years in public schools following the national curricula, revealing their roles in the PYP system. Drawing from interviews with PYP teachers, this case study elucidates the contrasting approaches to establishing product-process control models within these educational frameworks, as seen through the lens of teacher roles. The analysis serves to enhance comprehension of the rationale behind the PYP programme and is anticipated to offer novel perspectives on the national curriculum. The findings from the study underscore notable disparities between the two curricula in terms of their scoping objectives, patterns of curriculum control, assessment methodologies, and teacher roles. Through the accounts and viewpoints of the interviewed teachers, it was deduced that the IB-PYP curriculum within the Didaktik perspective can serve as an inspiration for future Turkish national curricula.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".