Bibliographic record
Abstract
Despite the predictions of some Western experts, Islam and nationalism were not among the major factors behind the collapse of Communism and the break-up of the USSR. 1 So, when in December 1991 the Soviet Union ceased to exist, the bulk of Soviet Muslims, with the exception of a small faction among the Chechens, Tatars, Uzbeks and Kazakhs, were bewildered and frustrated by the new political realities. As a result of the disintegration of the Soviet state, over seventy million Muslims, constituting about a quarter of the total population of the USSR, found themselves in different political entities represented by newly independent states of Russia, Azerbaijan, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan, as well as other former Soviet republics which had Muslim minorities. Russia’s Muslims, who made up between twelve and twenty million, were overnight cut off from their more numerous co-religionists in Central Asia and Transcaucasus and transformed into Russia’s religious minority. Since this book focuses on the Muslims of Russia, analysis of the situation in the former Soviet Muslim republics which acquired political independence from Moscow is left aside. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".