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Record W2916436006 · doi:10.53095/88975004

Economic Freedom of North America 2022

2022· report· en· W2916436006 on OpenAlexaboutno aff
Dean Stansel, José Torra, Fred McMahon, Ángel Carrión-Tavárez

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic freedomIndex of Economic FreedomPayrollIndex (typography)Government (linguistics)State (computer science)EconomicsInvestment (military)BusinessEconomic policyEconomic growthPolitical scienceMarket economyLawAccounting

Abstract

fetched live from OpenAlex

Economic Freedom of North America 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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.674
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0360.003

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.

Opus teacher head0.040
GPT teacher head0.232
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations68
Published2022
Admission routes1
Has abstractyes

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