Bibliographic record
Abstract
One way of understanding the modern world is to view it as broken up into rival political and economic blocs that compete for resources and markets through political, economic, and military power. Today, governments of energy consuming nations worldwide are concerned about the security of their energy needs more so than at any other time since the oil crises of the 1970s. Additionally, issues such as environmental stewardship, corporate social responsibility, sustainability, and human rights are factors in the contemporary energy debate. According to the International Energy Agency (IEA), in 2008 China produced 190 million metric tons (Mt) of oil, unfortunately the Chinese were net importers of 159 Mt of oil. What energy policies is China adopting to bridge this gap, and what does this mean for the United States? This paper examines various aspects and inter-relationships of energy security through a geopolitical lens, beginning with a discussion of the supply and demand of crude oil, and an attempt to understand energy security. It also places China in an evolving world energy matrix, examine China’s relationship with the United States and the future of Chinese/U.S energy and security policy concerns, and discusses the future of Chinese energy policy and security.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".