Bibliographic record
Abstract
I grew up in a family that left Communist Poland in the 1980s, and so it seemed a little unusual to me that I became interested in giving a voice to Polish Canadian Communists and their movement.Several factors motivated this decision.My family settled in Roncesvalles Village in the heart of Toronto's old Polish community along Roncesvalles Avenue, where most major Polish community organizations established their presence after World War II.The streets were lined with Polish deli shops, bakeries, and homestyle restaurants that offered everything from cabbage and potato-stuffed pierogi to regional kiełbasa and barszcz.Roncy is still a bustling neighbourhood, and although it has retained much of its Polish flare, the population has profoundly changed.When we lived there, the community was inhabited mainly by ex-soldiers, displaced persons, and former refugees who had refused to return to People's Poland after the war, and it was replenished in the 1980s by "solidarity wave" immigrants, such as my parents, who supported Lech Wałęsa and the anti-Communist movement in Poland.I grew up in a patriotic neighbourhood (and household).As a child, I was encouraged to read the Three Polish Bards, Adam Mickiewicz, Juliusz Słowacki, and Zygmunt Krasiński; I learned about important Polish leaders such as Józef Piłsudski and Tadeusz Kościuszko; and almost every year my family gathered at the Katyn memorial to commemorate the murder of over 20,000 Polish officers and intellectuals by the Soviets in 1940.Many Poles in the area organized patriotic folk dances, music festivals, and picnics, and most Polish parents sent their children to Polish-language school.List of Tables and Figures xv
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.588 | 0.413 |
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".