Bibliographic record
Abstract
Throughout the previous chapters, I have alluded to the Eastern Europeanist error of imagining an unbridgeable contrast between Eastern and Western Europe, a trick of the imagination accomplished by leaving out Central Europe in the middle. A major task of this book is to restore Central Europe to the picture. The restorative surgery that is required commands us to delve in some detail and to some depth into the semi-peripheral area occupied by Central Europe between the West and the East. In this chapter, there will be statistics and figures: I concentrate here on quantitative data. The picture that will emerge consistently is first, that Central Europe is located on many economic and cultural measures somewhere in the middle between West and East, though typically closer to the West; and second, that, at least in Central Europe (but probably also farther East), the things that are said about ‘Eastern Europe’ are mostly false, even though they may have an element of truth in them. They are half-truths. As Marshall McLuhan once quipped, ‘There is a lot of truth in a half-truth’. While a half-truth is not a fact, it raises the question of what makes some, or many, believe that it is. Typically, it is the result of some true facts twisted into a false conclusion by the observer's expectations. What I will be fact-checking are some of the commonest Eastern Europeanist expectations. Some of them may or may not apply to other parts of Eastern Europe, including Russia, but are at most half-truths when applied to Central Europe. Often, when they do apply to Central Europe, they also apply to semi-peripheral areas in the West. Here is a partial list of them: • Freedom and democracy failed. • Corruption is beyond control. • Poverty is rampant. • There are gangsters and prostitutes everywhere. • People are surly and miserable. • Outmigration is draining the population. As we examine these common Eastern Europeanist stereotypes, we must insist on avoiding two methodological traps. One is to assume a priori that the facts will differentiate between East and West, and then to arrange them so that this appears to be true. In discussing current affairs, this Eastern Europeanist error can make us view recent European history ‘through the lens of the Cold War, more than twenty-five years after it ended’.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.052 | 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 teacher head, 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".