WP series of the Math Stagnation Nation series, for New Zealand (over the past 15 -20 years and how to overcome this with MMU series)
Bibliographic record
Abstract
In this short paper, the author concisely demonstrate the math stagnations of the national average of New Zealand over the past 12 years (for PISA math) and 20 years (for TIMSS math) and provide the evidence -based solution that can overcome the math stagna tions completely within 1 administration using MMU 1 (to raise the worst half math average to the best half math average) or MMU 0.5 (with the half of the capacity of MMU 1) . The highlights of the demonstrations are: 1) New Zealand – along with virtually all other English -speaking developed countries – have been in deep math EDU growth stagnations (and even declines) over the past 15 to 20+ years. 2) Almost uniform math stagnations and declines of all 8 jurisdictions in PISA math 3) A set of solution proposal called MMU 0.5 or 1 (roughly boosting the jurisdiction or national math average by 0.6 - 0.7 Standard Deviation or
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.020 |
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".