Migration Narratives from Third Wave Bulgarian Immigrants in London, Canada: Internalization of Balkanism and its Effects on Citizenship
Bibliographic record
Abstract
Bai Ganio, a brash fictional character famous among Bulgarians who grew up during communism, has become something of an example of what not to do for Bulgarian immigrants to Canada aged thirty-five and up. Subconsciously, they tend to model citizenship and moral behaviour opposite to his as they work to integrate into Canadian society. Interviews with eight Bulgarian immigrants in London, Ontario who arrived between 1999 and 2005 were conducted with a focus on their migration narrative. A cross-chronotopic lens was applied to better understand how internalization of ‘the Balkan other’ (Bai Ganio) and their invisibility as white ethnic immigrants are presented in several scales. Bai Ganio, created by Aleko Konstantinov around the time of independence from Ottoman Rule, represents Bulgaria’s longing to become a part of Europe. The figure gained popularity again in the 1990s after communism ended in 1989 when Bulgarians were free to move to wherever they wished. The participants in this research think of themselves as being among the intellectuals whose leaving caused a brain drain from Bulgaria. This thesis argues for the importance of drawing on Bulgarian history when having conversations about their migration because it reveals the internalization of their image as a non-modern “others” and how they orient to it. Analyzing their narratives through the framework of chronotopes, which tie in aspects of time, space, and figures of personhood, further reveals how the same dynamics of understanding their identity in different spaces and time is constantly being presented in multiple scales as the United States, Bulgaria and Canada are in constant relation in their narratives.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".