Crude oil prices: A curse or a blessing for small businesses in Alberta?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".