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Record W4230596393 · doi:10.3138/cpp.36.suppl.s1

Unanticipated Outcomes: Lessons from Canadian Automotive FDI Attraction in the 1980s

2010· article· en· W4230596393 on OpenAlexvenueaboutno aff
Greigory Mordue

Bibliographic record

VenueCanadian Public Policy · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsAutomotive industryForeign direct investmentGovernment (linguistics)Agency (philosophy)Public policyCorporate governanceIncentiveRelevance (law)AttractionInvestment (military)Foreign policyBusinessEconomicsIndustrial organizationMarket economyEngineeringPolitical scienceFinanceMacroeconomicsEconomic growthSociologyLawPolitics

Abstract

fetched live from OpenAlex

In the 1980s, the Canadian automotive manufacturing industry grew from three significant players to eight, growth that was facilitated by public policy schemes that were bold, calculating, and provocative. The decision to introduce direct incentives was pivotal, generating anxiety at both the federal and provincial levels. However, the evolution of a series of additional policy tools, each holding tangible value, proved just as important. These included waiving Auto Pact liabilities, the introduction of targeted duty remission plans, adjustments to Voluntary Export Restraints, and manipulations of the Foreign Investment Review Agency. These elements were under the management of Canada's federal government, making it a far more active participant in automotive foreign direct investment (FDI) attraction than its US equivalent. While some observers believe that the approach that public policy-makers brought to automotive FDI attraction during the period this article explores might hold lessons for present day practitioners, the reality is that the evolution of global governance structures precludes access to many of the tools that were deployed with such effect in the 1980s. Despite the subsequent changes, the relevance of coherent, well-timed industrial policy endures.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.102
GPT teacher head0.271
Teacher spread0.169 · 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.

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

Citations7
Published2010
Admission routes2
Has abstractyes

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