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Record W4308982933 · doi:10.3280/efe2022-001003

Crude oil prices: A curse or a blessing for small businesses in Alberta?

2022· article· en· W4308982933 on OpenAlexaboutno aff
Salah U‐Din, Usman Sadiq

Bibliographic record

VenueECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsCurseBlessingRevenueEconomicsPopulationCrude oilEntrepreneurshipBusinessAgricultural economicsEconomyMonetary economicsFinanceGeography

Abstract

fetched live from OpenAlex

Small and medium enterprises (SMEs), entrepreneurship, and crude oil are important contrib- utors to the economic growth of several countries. Crude oil revenue facilitated the develop- ment of other economic sectors including SMEs in many countries and became a blessing for economic stability. However, in some countries or regions, it attracted most of the labour, capital, and government support at the cost of other economic sectors and became a curse. This study investigates the relationship between crude oil prices and small business entrepre- neurship activities in the province of Alberta. The Ordinary Least Square (OLS) models, along with some other statistical tools, are used to analyze the data for the period 1988-2018. Our findings reveal a positive relationship between crude oil prices and the number of small busi- nesses in Alberta and Canada, which is consistent with the natural resources blessing hypoth- esis. However, some labour-intensive and low-wage small business sectors were found to be negatively associated with crude oil prices. Moreover, the population growth and market in- terest rate hampered small business entrepreneurial activities, while GDP growth promoted them. Some implications are provided at the end of the study to diversify the economy of Alberta through promoting small business entrepreneurial activities.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

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

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 head0.019
GPT teacher head0.189
Teacher spread0.170 · 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 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
Published2022
Admission routes1
Has abstractyes

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