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Record W113009939

Whitefish: An Economic Primer

2009· preprint· en· W113009939 on OpenAlexaboutno aff
Arthur J. Hosios, Lawrence B. Smith

Bibliographic record

VenueRePEc: Research Papers in Economics · 2009
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsnot available
Fundersnot available
KeywordsOddsLimit (mathematics)Economic modelEconomicsConsumption (sociology)Compensation (psychology)Positive economicsRange (aeronautics)Economic dataEconometricsMathematical economicsPublic economicsMicroeconomicsComputer scienceSociologyMathematicsSocial scienceMacroeconomicsSocial psychologyEngineeringPsychologyLogistic regression
DOInot available

Abstract

fetched live from OpenAlex

The 2007 Ontario Court of Appeal decision in Whitefish Lake Band of Indians v. Canada (Attorney General) sets out, for the first time, a set of guidelines for establishing compensation for cases in which the Crown allegedly breached its fiduciary duties to a First Nation band. The influence and financial implications of this decision are far reaching, extending well beyond the Ontario courts. Indeed, going forward, it is difficult to imagine a case in Canada of breach of duty by the Crown in which the Crown or Plaintiff's arguments regarding compensation will fail to take account of the Whitefish decision. Nonetheless, a consensus concerning this decision has not yet emerged; all parties agree that Whitefish is important and yet, for the same set of circumstances, the financial implications of Whitefish are said to differ, not just between the Crown and Plaintiff’s positions but sometimes also among the "experts". This would seem to suggest that the implications and implementation of the Whitefish guidelines are legitimately open to different interpretations. This article demonstrates that this view is wrong. It establishes that the only internally consistent and empirically grounded framework for implementing Whitefish is the standard economic model of consumption-saving decision making. Other interpretations implicitly make assumptions about individual behaviour that are at odds with known data, rational economic behaviour and common sense. The article also clarifies which historical data need to be applied to limit the range of feasible compensation, consistent with the application of the standard economic model, for any given case.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.597
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.056
GPT teacher head0.307
Teacher spread0.251 · 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 designTheoretical or conceptual
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

Citations0
Published2009
Admission routes1
Has abstractyes

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