Bibliographic record
Abstract
A book with a foreword by Pat Clawson of the National Defense University and editor of ORBIS, and dedicated to Ronald Reagan and Target Ozxal, announces its U.S. far-right wing political pedigree literally up front. However the book is chock full of information, alas most already well known to anyone even remotely familiar with the problematique under review; but it also offers some incisive analysis. The twelve contributed chapters by fourteen authors and coauthors are divided into three parts dedicated to examining and analyzing the general history and mutual background of the Caspian Sea region; to the ?ve littoral states of Azerbaijan, Russia, Iran, Kazakhstan, and Turkmenistan; and to three external interested states, the United States, Turkey, and Georgia. Nonetheless, the review by each author goes well beyond the nominative boundaries assigned to him or her and trespasses over into the topics, territories and their relations assigned to other authors. Quite prop-erly so, in view of the mutually complex real-life interrelations in the Caspian Sea Basin, so that no topic or state could be adequately understood in itself other than in relation to the others. Indeed, we are witnessing the contemporary continuation of the nineteenth century Great Game for the control of Central Eurasia. However, the oil connection also reaches well beyond Caspian Sea and must make this book pertinent also to readers of this journal.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".