Bibliographic record
Abstract
are likely still unaware of this story and its place in their lives.These stories have been brought together with photographs I took of the twenty-four internment and holding camp sites across Canada, historical photographs, documents of the day, and my own writing.In my writing, I consider where Ukrainian internees had come from and House of Commons debates before the passing of the War Measures Act; I give brief information on the internment and national recognition of the internment, and I dialogue with the internees and their descendants across cultures to model memory work for readers who have the desire to empathetically locate themselves in these stories.I have focused largely on the internment of enemy aliens in Canada who were civilians from Bukovyna and Galicia, the part of Ukraine that was ruled over by the Austro-Hungarian Empire, which was at war with Britain. 1 In government documents, they were also called Austrians, Ruthenes, Rusyns, or Ruthenians, which led to confusion with officials.In this book, i u n d e r s t a n d reconciliation is a self-reflexive process, a search for the individual within history so that future generations can locate themselves in it.This book creates a space to reflect on the stories of individual internees and their descendants and to consider how lives are shaped by stories that are suppressed.Several of the names of descendants who told me their stories were brought forward by members of the Ukrainian Canadian Civil Liberties Association (uccla).Others were recommended by members of the Descendants of Ukrainian Canadian Internee Victims Association or came from leads that emerged unexpectedly when people found out I was working on this book.The stories told are personal.They are told by internees who suffered the consequences of the internment camps and their descendants who continue to learn from the experiences of their kin.The list of descendants is far from complete.With more than 80,000 people who had to carry certificates of registration and with 8,579 interned, that list would be sizeable after a hundred years.Many of those descendants
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.566 | 0.362 |
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".