Bibliographic record
Abstract
T he North American economy in 1999 was made up of 395 million people, 193 million workers, and $10.3 trillion of annual GDP. According to the OECD, in the U.S. economy, 269 million people and 139 million workers produced $9190 billion in GDP in 1999. The 30 million people and the 16 million workers of Canada produced $624 billion in GDP. The 96 million people and 38 million workers of Mexico produced $475 billion in GDP. This continental economy is staggeringly unequal. Less than 4 percent of Canadian workers and less than 3 percent of U.S. workers are in primary sectors— agriculture, fisheries, and forestry. But one out of five of Mexico’s workers is in the primary sector. GDP per capita in the United States is some $33,900. GDP per capita in Canada at recent exchange rates is $20,400. GDP per capita in Mexico is $4900. Using purchasing power exchange rates to calculate per capita GDP rather than market exchange rates does close the gap a bit. It raises Canada’s per capita GDP to $25,900 per year and Mexico’s to $8100. Even so, the U.S. level is one-third higher than Canada and quadruple that of Mexico. Moreover, within Mexico, the contrasts between the richest portions of the industrial north and the poorest portions of the near-subsistence south are as great as the contrasts between Mexico and the United States. The economic differences between the three nations are, in many ways, greater than the differences in other measures of well-being. Female life expectancy at birth in Mexico at 77 years compares not unfavorably to the 79 years of the U.S. and the 81 years of Canada. Infant mortality in Mexico at 16 per thousand is worse than the eight per thousand of the United States or the six per thousand of Canada—but the gap is not as wide as one might expect based on the per capita GDP figures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.064 | 0.016 |
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".