Education and the Revolutionary Personality: The Case of Ilona Duczynska (1897–1976)
Bibliographic record
Abstract
The autobiographical writings of the sometime Canadian resident Ilona Duczynska (1897–1978), born near Vienna of a Polish father and Hungarian mother (both of the lower nobility), were designed to show how experiences within the family during childhood and youth led to her becoming a revolutionary. Duczynska claimed to have experienced a species of class struggle—involving the families of her idealized father and her much criticized mother—that brought about the death of the former and marked her personally with the sign of inferiority. It followed, then, that education was powerless to amend what Duczynska decided she had already ‘learned’ within the family, including her malcontent father’s characteristic spirit of negation. Consequently, Duczynska describes the various stages of her distinctly privileged education in Austria, Germany, Switzerland and Hungary—almost entirely in terms of how she availed herself of opportunities to take a ‘stand’ against existing institutions. Inevitably, in 1922 even the ‘party school’ of the Hungarian Communist Party forfeited her confidence. Further research, drawing on psychological insights, may show why Duczynska’s family experiences should have led to a mistrust of the family as an institution, fascination with ‘revolutionary violence’, and life-long hatred of liberal democratic (and capitalist) institutions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.019 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".