Bibliographic record
Abstract
In recent years, over one million Canadians have claimed Polish heritage - a significant population increase since the first group of Poles came from Prussian-occupied Poland and settled in Wilno, Ontario, west of Ottawa in 1858. For over a century, descendants from this community thought of themselves as Polish, but this began to change in the 1980s due to the work of a descendant priest who emphasized the community’s origins in Poland’s Kashubia region. What resulted was the reinvention of ethnicity concurrent with a similar movement in northern Poland. Creating Kashubia chronicles more than one hundred and fifty years of history, identity, and memory and challenges the historiography of migration and settlement in the region. For decades, authors from outside Wilno, as well as community insiders, have written histories without using the other’s stores of knowledge. Joshua Blank combines primary archival material and oral history with national narratives and a rich secondary literature to reimagine the period. He examines the socio-political and religious forces in Prussia, delves into the world of emigrant recruitment, and analyzes the trans-Atlantic voyage. In doing so, Blank challenges old narratives and traces the refashioning of the community’s ethnic identity from Polish to Kashubian. An illuminating study, Creating Kashubia shows how changing identities and the politics of ethnic memory are locally situated yet transnationally influenced.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".