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Record W3122033895 · doi:10.29173/jaed334

Institutional Change On First Nations: Examining Factors Influencing First Nations’ Adoption of the Framework Agreement on First Nation Land Management

2013· article· en· W3122033895 on OpenAlexaffabout
Mary Doidge, Brady J. Deaton, Bethany Woods

Bibliographic record

VenueJournal of Aboriginal Economic Development · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsUniversity of Guelph
FundersAustralian Government
KeywordsProbit modelGovernment (linguistics)AutonomyOrdered probitVariable (mathematics)EconomicsProbitFirst nationPublic economicsEconomic growthPolitical scienceGeographyLawEconometricsMathematics

Abstract

fetched live from OpenAlex

In 1999 the Canadian Federal government passed the First Nations Land Management Act, ratifying the Framework Agreement on First Nation Land Management signed by the government and 14 original signatory First Nations in 1996. This Agreement allows First Nations to opt out of the 34 land code provisions of the Indian Act and develop individual land codes, and has been promoted as a means of increasing First Nation autonomy and facilitating economic growth and development on reserve lands. There are currently 77 First Nation signatories to the Agreement, 39 with operational independent land codes. This paper is the first to empirically examine factors that may influence a First Nation's decision to become signatory to the Framework Agreement. A unique dataset characterizing each First Nation by socio-economic and demographic characteristics is used with a probit model to determine the effects of these characteristics on the probability of First Nation adoption of the Agreement. The results of this study indicate that proximity to an urban centre positively affects the probability that a First Nation will adopt. However, the statistical strength of this finding is sensitive to the inclusion of an education variable in the regression.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.092
GPT teacher head0.233
Teacher spread0.141 · 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 teacher head, 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

Citations1
Published2013
Admission routes2
Has abstractyes

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