Millennium Development Goals: update from North America
Bibliographic record
Abstract
In the past 15 years, North America made significant advances towards the Millennium Development Goal (MDG) targets despite a crippling and global financial crisis beginning in 2008. The three countries making up North America—Canada, the USA and Mexico—aspired to reach MDGs under strikingly different political and cultural contexts; yet within their own countries, they all face the common challenge of reducing the disparities that exist in education, employment and health across socioeconomic, racial and gender spectrums. Progress towards reduction of disparities in poverty and hunger is evident in North America. Mexico met its goal to cut in half the percentage of people living off <$1.25 per day 3 years before the anticipated date. This reduction is consistent with the change in percentage of people living in poverty across Latin America, decreasing from 12% in 1990 to 6% in 2010. Middle-income countries, however, including Mexico, Argentina, Brazil, Chile and Peru, are largely responsible for this change. The USA achieved a historic change in the lives of people living in poverty with the adoption in 2010 of the Affordable Care Act (ACA) providing access to healthcare for all citizens. ACA will improve healthcare access; however, it will not impact non-medical factors, such as poverty, driving poor health outcomes in the USA. Income inequality in the USA and Mexico is currently at an all-time high and remains significantly higher than in European 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".