Bibliographic record
Abstract
As oil prices rose in 2004, a large part of the blame was laid at the feet of the emerging colossus of the East. Newspaper stories wrote of the, “surging,” and, “insatiable demand,” coming from China, describing it as the, “engine of oil demand growth,”1 and explaining the change, More than a billion Chinese are joining the oil market…. How can prices go down?”2 There were moderately dissenting voices, e.g., from a professional at the International Energy Agency, It is neither fair nor accurate to blame China for most of the rise in oil prices.3 The measured increases in China’s international oil imports are based on international data and are quite real and not related to the probable overestimates of China’s overall rate of economic expansion. The very high growth rates of Chinese oil imports in 2004 and previous years are shown in Table 1. The implied growth rates are so high as to be almost unbelievable. From the fourth quarter of 2003 to the third quarter of 2004, there was a 30 per cent increase in crude oil imports. Such a high growth rate is not the way economies, in general, actually behave or, in particular, the manner in which the Chinese economy has functioned in the past, even in the course of its remarkable expansion. Yet the growth is real, so how can it be explained? That is the puzzle!
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".