Economic Freedom of North America 2022 Full Dataset
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Full dataset of the Economic Freedom of North America 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 a subnational index for comparison of individual jurisdictions (provincial/state and municipal/local governments) within the same country, and an all-government index for comparison of jurisdictions (federal governments) in different countries. For the subnational index, Economic Freedom of North America employs 10 variables for the 92 provincial/state governments in Canada, the United States, and Mexico 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 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/state and municipal/local level.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.061 | 0.000 |
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 it