Bibliographic record
Abstract
The lyricism of Bread & Water interweaves culinary insights and literary essays to pose fundamental questions about how we live––and how we feed––the larger hungers that motivate our lives. “When I write of hunger, I am really writing about love and the hunger for it, and warmth and the love of it and the hunger for it . . .” —MFK Fisher When chef and writer dee Hobsbawn-Smith left the city for rural life on a farm in Saskatchewan, she planned to replace cooking and teaching with poetry and prose. But—as begin the best stories—her next adventure didn’t quite work that way. Food trickled into her poems, her essays, her fiction. And water poured into her property in both Saskatchewan and Calgary during two devastating floods. Bread & Water uses lyrical prose to examine those two fundamental ingredients, and to probe the essential questions on how to live a life. Hobsbawn-Smith uses food to explore the hungers of the human soul: wilder hungers that loiter beyond cravings for love. She kneads themes of floods and place, grief and loss; the commonalities of refugees and Canadians through common tastes in food; cooking methods, grandmothers and mentors; the politics of local and sustainable food; parenting; male privilege in the restaurant world; and the challenges of aging gracefully. It is an elegant collection that weaves joy into exploring the quotidian in search for larger meaning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.029 |
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".