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Record W2979381234 · doi:10.4324/9780429051333-9

Natural Resources, Economic Development, and U.S.-Canadian Relations: A Western Canadian Perspective

2019· book-chapter· en· W2979381234 on OpenAlexaboutno aff
Kenneth Norrie

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Natural resourceGeographyNatural (archaeology)Political scienceRegional scienceArchaeologyComputer scienceLaw

Abstract

fetched live from OpenAlex

The dislocation experienced in raw materials markets in the early 1970s has revived old concerns about the finiteness of natural resource supplies being the ultimate constraint to long-term economic growth. This development has, in turn, produced a renewed interest in policies designed to minimize the disruptive economic impact of any future resource shortages. The economies of the western provinces have always been intricately linked with the production and export of raw materials products to more industrialized areas. From the fur trade era through early timber and mineral production in British Columbia and grain exports from the Prairies to the current reliance on agricultural products, timber, oil and gas, and metallic and non-metallic minerals, the four western Canadian provinces have consistently been classical staple economies. Saskatchewan provides an extreme example of a staple economy, with nearly total dependence on agricultural exports for its well-being.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.163
Threshold uncertainty score0.971

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.012
Science and technology studies0.0120.006
Scholarly communication0.0070.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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