WP series: Mathematics Stagnation Nation series: for the USA (Part 5. Draft 2) The quasi-universal math stagnations in developed countries are real and won't go away as the conventional EDU reforms are mostly futile: how to transcend them with MMU1 or at least 1/3 of its full version over the next 2- 4 year
Bibliographic record
Abstract
In this short, visual data rich paper , we will demonstrate the following: 1) the math EDU stagnations in almost all OECD nations (especially in the Western countries) are here to stay and they will not go away according to the data from PISA, TIMSS internationally and NAEP for the USA; 2) as the Math growth is critical for the modern economic growths and yet the EDU establishments are highly inefficient and adhere to the traditional alternatives instead of embracing more unconventional approaches, we provide a wide variety of the reality -biting re sults; 3) throughout the paper in this series, we used the yellow arrows as the expected math growth estimations against the past historic math growth data from the international and national math tests to demonstrate to the readers as to what they are mis sing by simply looking at the other directions when the answer is here already; 4) our mantra: to end math poverty means to end poverty itself. As such, we focus primarily on
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.155 | 0.131 |
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".