Economic Freedom of North America 2024 Subnational Dataset
Bibliographic record
Abstract
Economic Freedom of North America 2024 measures the degree to which governments in North America permit their citizens to make their own economic choices. The subnational dataset of the report encompasses data from the 10 Canadian provinces, the 50 U.S. states, the 31 Mexican states and Mexico City, and the U.S. territory of Puerto Rico, for comparison of all 93 individual jurisdictions (provincial, state, and local governments) within the same country. Canada’s most economically free province is Alberta. The next highest provinces in the subnational index were Ontario and Manitoba, followed by Newfoundland & Labrador, and British Columbia. In the U.S., New Hampshire earned the top spot again this year. South Dakota rose to second and Florida fell to third, followed by Tennessee and Texas. Puerto Rico is the lowest-ranking U.S. jurisdiction by far. The most economically free of the Mexican states is Michoacán de Ocampo, followed by Baja California, Morelos, Jalisco, and Puebla.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.020 |
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".