Economic Freedom of North America 2023 Full Dataset
Bibliographic record
Abstract
Full dataset of the Economic Freedom of North America 2023 report that measures the extent to which the policies of individual provinces and states are supportive of economic freedom—the ability of individuals to act in the economic sphere free of undue restrictions. It includes an all-government index for comparison of jurisdictions (federal governments) in different countries and a subnational index for comparison of individual jurisdictions (provincial/state and municipal/local governments) within the same country. For the subnational index, Economic Freedom of North America 2023 employs 10 variables for 92 provincial and state governments in Canada, the United States, and Mexico, and for the US territory of Puerto Rico in three areas: (1) Government Spending, (2) Taxes, and (3) Regulation. In the case of the all-government index, we incorporate three additional areas at the federal level from Economic Freedom of the World: 2023 Annual Report: (4) Legal Systems and Property Rights, (5) Sound Money, and (6) Freedom to Trade Internationally. In addition, we expand Area 1 to include government investment, Area 2 to include top marginal income and payroll tax rates, and Area 3 to include credit market regulation and business regulations. These additions help capture restrictions on economic freedom that are difficult to measure at the provincial or state and municipal or local level.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.030 |
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".