Bibliographic record
Abstract
Previous studies on macroeconomic effects of oil price changes and of oil price uncertainty have mainly focused on real economic outcomes, such as employment, consumption and aggregate output. This paper investigates the links between the oil price changes and the consumer confidence in Canada, at both the national and the regional levels, using the correlation analysis and least square econometric methods. The results of this paper suggest the oil price declines mainly affect the consumer confidence about the overall employment and about the overall buying conditions of durable goods. More specifically, the oil price declines have negative impacts on the consumer confidence about the overall employment in Canada. However, the impacts on the consumer confidence about the overall buying conditions of durable goods differ in the oil-producing regions and the oil-consuming regions. For the minority of the regions in Canada, the oil price changes affect the consumer confidence about the household financial positions. In addition, several oil price transformations are examined in this paper. The introduction of the asymmetric oil price changes reveals that the oil price declines are more important in affecting the consumer confidence about the employment expectation than the oil price rises. Although different levels of the oil price volatility might not affect the consumer confidence, the higher is the oil price volatility, the more sensitive is the consumer confidence to the oil price changes of the same size.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".