WP series: Mathematics Stagnation Nation series for the USA (Part 2) Math (Grade 4 & 8) stagnations of the 50 states of the United States: national, states, and cities & Their past growth compared to the projected MMU1 impacts on math as upheavals
Bibliographic record
Abstract
In this visual timeline -driven observational report and analysis based on the math grades 4 and 8 of the NAEP (the National Re port Card) of the USA 1992 -2015 (during 23 years), we demonstrate that the following 7 key points : 1) for the 50 USA states, the math stagnations are real, not something to wisp away as the stagnations kicked in around 2003 -2007 to the majority of the USA states for the math grade 4; 3) the math stagnations seemed to kick in a bit of time lag for the math grade 8 some years after the math stagnations kicking in for the math grade 4; 4) we observed this for the entire 50 states a gainst the national (public) average timelines; 5) the math stagnations even for the fastest math growing states out of the 50 states; 6) outside the 50 USA states, DC and DoDEA haven't had full-blown math stagnations yet, but their growths have slowed dow n significantly around 2013 - 2015; 7) all their timeline math growths plotted against the expect MMU
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".