Review of <i>Leaving Shadows: Literature in English by Canada's\nUkrainians</i> By Lisa Grekul
Bibliographic record
Abstract
In the 1890s, when Canadian government officials first began a concerted effort to settle Canada's prairie provinces, they sent envoys to Eastern Europe, most notably to the area that is now Ukraine, to find people willing and able to accept the harsh conditions of the prairie region. Thus came the first large wave of Ukrainian immigration to Canada, followed by two others: during the Great Depression and immediately after World War Two. How has this large contingent of settlers fared in the Canadian imagination, specifically in its literature, as succeeding generations, born and raised in Canada, grapple with their dual heritage? Lisa Grekul addresses this question in clear, concrete fashion by examining the representation of Ukrainians in the work of various Canadian authors, in particular those who are descendants, as she is, of the first great wave of settlers to the prairies. In addition to scholars studying the prairies, her work is invaluable to all those interested in ethnic studies, literary or otherwise, of what Canada calls "invisible minorities," that is, those ethnic groups who are not differentiated by race from a nation's dominant culture, and are therefore more easily assimilated by (or, more negatively, disappear into) that larger culture.\nThe book is organized thematically and chronologically into three main parts, beginning with the portrayal of Ukrainian settlers in works written by Ralph Connor, Sinclair Ross, and Margaret Laurence. Grekul includes them because, in the first half of the twentieth century, there were no fictional works written in English by the settlers themselves. The first such work, by Vera Lysenko, closes the section, providing a firm foundation for the works following, written by the generations descended from all three waves of Ukrainian immigration to Canada. The second part uses the fiction and poetry of Maara Haas, George Ryga, and Andrew Suknaski to explore the ways in w~ich the second generation both incorporates and moves away from the first one. The third section focuses on the works of Janice Kulyk Keefer and Myrna Kostash, considering the ways in which, several generations removed from the "Old Country," these writers grapple with their dual heritage. The works of less well-known and prolific writers are discussed throughout the book as well, and Grekul's citations are a thorough compendium of scholarship on the subject.\nIn the introductory and concluding chapters, Grekul reveals the dilemma of ethnic writing from a very personal perspective, one lending poignancy to the negative conclusion she draws about the future of ethnic literature within the larger literary community. Because the Canadian arbiters of multiculturalism have made race "the locus of debate about identity," Grekul believes that literature by all "invisible minority" ethnic groups will remain marginalized and therefore "meaningless." In writing this book, she argues most persuasively for their importance to, and inclusion in, this continuing debate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".