SOURCES OF UKRAINIAN CANADIAN IDENTITY IN JANICE KULYK KEEFER´S NOVEL THE GREEN LIBRARY
Bibliographic record
Abstract
The present thesis discusses the sources of Ukrainian-Canadian identity based on Janice Kulyk Keefer´s novel The Green Library. The chief aim of the thesis is to try to understand what identity is, how one can identify oneself in terms of culture, and most importantly, what methods are available for this as well as what influences the choice of the methods. The Green Library represents the experience of the second generation Ukrainian immigrants who are struggling with their identity formation. The novel has been chosen as a perfect example to explore these identity issues. The Green Library presents contrasting approaches to the concept of identity. As a result, the main characters of the novel have different strategies of acculturation. These diverse strategies are discussed in greater detail. The thesis shows that identity cannot exist on its own, but it is influenced by various factors and formed in the process of a series of identifications. The present thesis consists of four parts: the introduction, two chapters and the conclusion. The introduction states the importance of the theme to be discussed. It raises the questions about identity formation and the sources of identity to be answered in this
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.030 | 0.011 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".