«Yet families are more than gene pools: their stories travel through and map us,too»: Janice Kulyk Keefer’s Honey and Ashes: A Story of Family
Bibliographic record
Abstract
Honey and Ashes () revolves around a quest for identity, national and personal; more precisely around a quest for lost origins. In keeping its focus throughout the narrative on family snapshots and studio portraits this family memoir of Ukrainian Canadian author Janice Kulyk Keefer foregrounds the role of photographs in recovering and reconstructing memories of a lost past. Keefer was born in Canada, but her parents and grandparents have a history of painful displacement from their mother country, a place she experienced vicariously through the memories of her relatives, her mother’s above all. The photos help her to give concrete evidence of their story, from which she was, in actual fact, excluded. In Honey and Ashes Janice Kulyk Kefeer explores the issues that haunt those who live simultaneously in two countries: the country of the heart and that of the mind. The author was born in Canada, a country which for her family and herself meant freedom and a future filled with opportunities. She was also born, however, into the history of her family’s homeland, Ukraine, thus inheriting both the gift and the burden of her family’s homeland past, a past that is perceived as «an equal spill of beauty and blood». The painful, disconcerting aspects of this process of remembering are remarked upon throughout the text:
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.017 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".