MétaCan
Menu
Back to cohort
Record W4414261491 · doi:10.4324/9781032712185-34

Academic Job Placement in Post-Communist Studies

2025· book-chapter· en· W4414261491 on OpenAlexaboutno aff
Ivan Katchanovski

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsOddsExtant taxonProxy (statistics)UkrainianPosition (finance)Western europe

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.732
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.049
GPT teacher head0.381
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicHigher Education Governance and DevelopmentFrench-language works237,207