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Record W4392827654 · doi:10.29173/jaed108

Managing Saskatchewan's Expanding Aboriginal Economic Gap

2000· article· en· W4392827654 on OpenAlexaffabout
Marv Painter, Kelly Lendsay, Eric Howe

Bibliographic record

VenueJournal of Aboriginal Economic Development · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBusinessGeography

Abstract

fetched live from OpenAlex

katchewan and to forecast those gaps over the next 50 years .Each of the gaps is estimated as the difference between average Aboriginal income, employment, education, and economic levels and the corresponding levels for the total Saskatchewan population.The second objective is to discuss the economic and social implications of the growing and unsustainable Aboriginal economic gap in Saskatchewan by assessing the impact on Aboriginal people, nonaboriginal people, governments, and the business community.The third objective is to discuss possible approaches for managing the Aboriginal economic gap over the next 50 years. MethodologyThe economic forecast for Saskatchewan (1995-2045) was prepared using PREMOS, a mediumsized macroeconomic model developed by Eric Howe. 2 PREMOS examines economic scenarios from a Saskatchewan provincial perspective only.It makes projections about the overall Saskatchewan economy and the relationship between economic variables.PREMOS predicts Saskatchewan's economic future using a system of over one hundred simultaneous equations.Many of the equations of PREMOS are common sense.For example, the consumption function shows how consumption in Saskatchewan varies in response to disposable income; the government spending equation shows how government spending varies in response to government revenues and the demand for government services; and the migration equation shows how migration depends on the relationship between provincial labour demand and supply.The model forecasts the major provincial economic variables such as gross domestic product, consumption, investment, employment, disposable income, and industry employment.There are four exogenous components: the federal government, the natural resources industry, interest rates, and the Canadian labour market.There are five endogenous components: population, expenditure, labour demand, income, and provincial government revenue and expenses.The output from PREMOS was combined with empirical data gathered from a variety of sources, including Statistics Canada, Federal Department of Indian and Northern Affairs, Aboriginal Business Canada, Aboriginal Peoples Survey (1991), Report on The Royal Commission on Aboriginal Peoples, and the Government of Saskatchewan Indian and Metis Affairs Secretariat.These data were used as a starting point for the economic forecast as well as to disaggregate the economic output variables into Aboriginal and Non-Aboriginal categories.

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 categoriesInsufficient 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.999

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.009
GPT teacher head0.272
Teacher spread0.263 · 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
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
Published2000
Admission routes2
Has abstractyes

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