Bibliographic record
Abstract
For this thesis, I have written a series of interconnected short stories that tell the early life of Teklė, a Lithuanian immigrant to Canada in the 1930s. These stories are written alternately from Teklė’s point of view as well as that of different women who come in and out of her life. In this work, I aim to paint a picture of the ways women recognize and connect with one another through common experiences, in turn telling the story of Teklė’s hardships, unwavering hope, and determination to find family and place. Teklė is based loosely on my own grandmother, and each story in the collection centres around people and events that influenced her life. \n \nIn the women’s stories, I explore themes of grief, caretaking, marital conflict, child-rearing, and other responsibilities carried by women across generations. Each woman is compelled by Teklė’s presence to reflect on their own life, and in turn help Teklė to heal and move on. \n \nInterspersed are stories told from Teklė’s point of view, providing an opportunity to see her unfiltered at pivotal moments. Although the themes of abuse, scarcity, discrimination, and sexism run through the collection, Teklė remains a character with unshakable hope. Hers is ultimately the story of the female immigrant experience, where new lives are built on a foundation of trauma and tragedy that’s often walled away for survival. This collection is an attempt to remove some of the bricks for Teklė.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.046 | 0.030 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".