WP series: Mathematics Stagnation Nation series: for the USA (Part 4) Math Education stagnations in the USA played more roles than the Common Core math standards impacts for the stagnations on the NAEP 2015, but the math dipping (especially the grade 8) were most likely were due to the Common Core math
Bibliographic record
Abstract
This paper is an extension of the previous paper by the author on the theme of the math stagnations in almost all developed (OECD) nations internationally, for all developed English -speaking and most of the Latin Ameri can countries. The author has covered this theme for the USA math stagnations in the international math assessments, national NAEP's national math growth stagnations, most of the states' math stagnations , and at least 90 -95% of the large distri cts' (or cities' ) math stagnations over the past 5 -10-15-20 years. In this paper, the author o bserve s and demon strate s the following: 1) the longer the states had stayed with the Common Core math standards, the math grade 4 average and 25 percentile had de clined more than the USA states that had never participated in the Common Core math or those that had opted out by the end of 2014 or so before the NAEP 2015 math dipping happened for both the grade 4 and 8; 2) The similar pattern was also observed for
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.015 |
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".