Bibliographic record
Abstract
In recent years, interdisciplinary teaching has gained attention particularly through efforts to pursue STEM. Other approaches to interdisciplinary education bring other subject disciplines together. In this book an international selection of authors consider what happens when their subject disciplines are combined with mathematics. The approach is anchored by math education to provide a lens on the implications for a single area of subject content when an interdisciplinary approach is taken. The collection provides inspirational ideas for drawing content areas together but also demonstrates that there are deeper issues that the education field needs to consider. The theoretical issues that emerge show a need for improving the foundation for interdisciplinary approaches. This is an informative book that shows that there is a need for further study of how numeracy, the broader scope of mathematics, and an interdisciplinary philosophy of curriculum are entwined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".