Bibliographic record
Abstract
This chapter analyses factors that determine the academic placement of PhD graduates of US, British, and Canadian universities from East Central Europe and post-Soviet countries with specialisation in post-communist studies. The analysis shows that merit-related factors, such as the number of published refereed articles, significantly increase the odds of placement in a permanent faculty position in Western universities. However, male Belarusian, Russian, and Ukrainian doctoral graduates are significantly less likely than those from the other countries studied to secure such faculty positions in Western universities. The findings are strongly suggestive of discrimination against, and the deliberate exclusion of, male job candidates from these countries, which can be interpreted as part of the wider manufacture of consent in the Western countries supporting the proxy war in Ukraine. The strongest indicator of this is the extant lack of male Ukrainian political scientists in tenured positions in Western universities, that is, during the Russia–Ukraine war when detailed knowledge about Ukraine presumably is at a premium. Such discrimination is also inconsistent with declarations regarding the importance of ‘Ukrainian voices’ and the ‘decolonisation’ of post-communist studies in the West. The study raises questions about bias and the politisation of the study of East Central Europe and post-Soviet countries in Western academia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".