The Canadian Condition: Migration of Intellectuals and Artists in Post-Yugoslav Fiction
Bibliographic record
Abstract
Distancing herself from fixed national canons and identities, belatedly famous novelist Daša Drndić chose to occupy a political and artistic position of permanent exile.Therefore, her literary work will be examined in the context of the post-Yugoslav literary canon, which is still in the making.The paper will present Drndić's novels as part of a transnational, or supranational, corpus of literature and art that share thematic, stylistic, formal and ideological concerns, focusing on her Canadian experience.Her disappointing emigration period spent in Canada in the late 1990's (1995 to 1997) resulted in the semiautobiographical novel Maria Częstohowska Still Shedding Tears or Dying in Toronto (1997).Drndić's novel brings together diverse memories, notes and documents that testify to intellectual crisis, feelings of isolation and defeat as well as to a terrible humiliation lurking behind the apparent security and comfort offered to a displaced person.Daša Drndić narrates about her Canadian migrant experience as a series of losses: country, memory, language, art and identity.Displacement is too painful a condition to be simply shrugged off as a temporary crisis, but the author refuses to rescue herself by leaving the turbulent history of her lost homeland behind: she prefers to return to it with a renewed potential of both self-examination and suffering.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.037 | 0.017 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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; 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".