3 strategies of MMU1 from math poverty to math prosperity: if you can fly why do you run? If you can run, why do you walk? If you can walk, why do you crawl? You may be crawling and refusing to even walk, let alone to fly
Bibliographic record
Abstract
The whole game of MMU x.x that Lee proposes starts with Lee's mantra: "To end the math poverty is to end the poverty itself." Not only that, those who escape the math poverty spill over the math prosperity, which leads to the socio -economic prosperi ty quickly. The major difference is that Lee is trying to make the transitions happen in 2 -4 years instead of 50 -100 plus years if you go without MMU series. Before you start reading this document, I strongly suggest you to read the front page of www.uslgoglobal.com first, which may take about 5 minutes. Lee's basic incentive highlights to the school districts, cities, states, and national governments The following is mostly relevant for the OECD level developed countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".