Bibliographic record
Abstract
Racial capitalism requires that the subaltern periphery, providing cheap labour and new markets, be placed behind an imagined racial barrier, so that the full protection of the liberal state is not extended to it. This has applied also to the ‘Eastern enlargement’ of the EU. The East has had to compete with a much richer and more powerful West. When, inevitably, the East was unable to ‘catch up’, its ‘failure’ was attributed to its alleged historical and cultural incompatibility with the West. Such racist discourse has penetrated global and European politics, economics, and media. It also affects people who move from the East to the West. Unfortunately, many Eastern Europeans project their own racialisation onto others. This dynamic is articulated from the equivocal position of Eastern Europe, between the core West and the Global South. It aims to affirm the threatened whiteness of people in the region by distancing them from the Global South. But also, it functions within Eastern Europe, with each country to the East imagined as more ‘Eastern European’ until one reaches the prototypical Eastern European nation, Russia. For racism against Eastern Europeans reflects, in the final analysis, the long-standing imperial rivalry between the West and Russia.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".