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Record W4387026612 · doi:10.3386/w31713

Institutional Drift, Property Rights, and Economic Development: Evidence from Historical Treaties

2023· report· en· W4387026612 on OpenAlexaffabout
Donna Feir, Rob Gillezeau, Maggie Jones

Bibliographic record

VenueNational Bureau of Economic Research · 2023
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of VictoriaUniversity of Toronto
Fundersnot available
KeywordsProperty rightsProperty (philosophy)Law and economicsPolitical scienceEconomicsLawEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

For nearly three centuries, Indigenous peoples within the borders of present-day Canada engaged in treaty-making with the British Crown and other European powers.These treaties regularly formed the colonial legal basis for access to Indigenous lands.However, treaties were not negotiated everywhere, including in regions subsequently settled by Europeans.Consequentially, there is substantial regional variation in the legal status of occupied lands, jurisdiction over natural resources, and state commitments to Indigenous nations.We study how these legal institutions have shaped the path of economic development in Indigenous communities.Using restricted-access census data, we show that historical treaties substantially lower income in Indigenous communities today.We argue that this results from the constitutional and legal recognition of Aboriginal rights and title, which have dramatically increased bargaining power and, consequently, income growth in non-treaty Indigenous communities.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.004
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.678
GPT teacher head0.448
Teacher spread0.230 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2023
Admission routes2
Has abstractyes

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