Postscript: Confessions of a Canadian Central European
Bibliographic record
Abstract
I wrote this book because I am a Central European, and in spite of its severe faults I love Central Europe. To be precise, I am a Central European, but not quite. I was born in Prague, by coincidence. I am not quite a Czech, though I do hold a Czech passport (as well as Canadian). My parents survived the Holocaust in Budapest, using false names and forged documents to convince police and collaborators that they were not Jewish. Neither my father nor my mother was quite a Hungarian. He was born in Slovakia, where Jews were not considered quite Slovak. She was a Budapest girl, but her father came from some place in the Habsburg province of Galicia that no one remembers. The family called it Poland, though today it may well be Ukraine. Although my grandfather was not quite Polish, his origins caused problems for my mother’s folks. They suffered discrimination by Hungarians, including Budapest Jews. The ‘modern’ Jews there had contempt for the ‘Eastern Jews’, as they called the Orthodox Jews from the eastern regions of the Habsburg Empire. They would have been surprised to hear, in what I say in this book is postwar terminology, that they themselves were ‘Eastern European’. My father, who had studied medicine in Bratislava before being expelled as a Jew, married my mother when the war was over, and they moved to continue his studies in Prague.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.004 |
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".