Bibliographic record
Abstract
The collapse of the Soviet Union has gradually drawn the natural resources of the Russian Far East into Northeast Asia's economic field of gravity. Contrary to what many experts predicted in the early 1990s, the opening of these natural resources to the wider region has generated a substantial dose of interstate friction. Since the turn of the century, much of this friction has revolved around Chinese and Japanese competition to secure hydrocarbon fuels through pipelines transecting the Russian Far East. This competition is mediated by a complex and contradictory situation: Moscow and Beijing are trying to bolster Russo-Chinese partnership through enhanced commercial exchange, but actors in Russian provinces bordering Northeast China remain wary of rising Chinese demographic and economic influence in the area. Nonetheless, the intransigent geopolitical disposition of the US and the changing configuration of world energy markets have given nearly irresistible momentum to expanded Russo-Chinese energy cooperation, even if this portends Russia's partial sidelining of Japan as a customer of east Siberian oil. However, it is still unclear whether expanded energy cooperation alone can pave the way toward genuine geo-economic integration in the Russian Far East—Northeast China border zone.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".