Bibliographic record
Abstract
Over the past 5 years, real energy and non-energy commodity prices have trended sharply higher. These relative price movements have had important implications for inflation and economic activity in both Canada and the rest of the world. China has accounted for the bulk of incremental demand for oil and many base metals over this period. As rapid economic growth in China has raised the level of world demand, this has put upward pressure on commodity prices. The effect has been amplified by rising resource intensities in China's production in recent years. This paper discusses the factors driving emerging Asia's demand for commodities and assesses the impact of emerging Asia on the real prices of oil and base metals in the Bank of Canada Commodity Price Index (BCPI). Two separate single-equation models are estimated for oil and the base metals price index. We employ a structural break approach for oil prices, while metals prices are modelled with an error correction model (ECM). In both cases, we find strong evidence that oil and metals prices have historically moved with the business cycle in the developed world, but that this relationship has broken down since mid-1997. Thereafter, industrial activity in emerging Asia appears to have become a more dominant driver of oil price movements. While metal price fluctuations have also become increasingly aligned with levels of industrial activity in emerging Asia, rising intensities of metal production may have been a more important factor behind the acceleration in prices in recent years.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".