Comfort Food in Hard Times: Intergenerational Connections
Bibliographic record
Abstract
Caught off guard when the Covid-19 pandemic struck in March 2020, people immediately began to search for models to help guide us through such uncharted territory. Nothing—from the history of the 1918 flu to the experiences of those living in conflict zones (Admad 2020)—seemed to hold the answer. On the other hand, endless posts of sourdough and other baking projects on social media, as well as the short supply of flour and yeast on grocery store shelves, soon made it clear that many people were turning to baking and cooking to help get through. In this article, I reflect on both these aspects—the lack of models and the importance of food—to my own household’s experience of COVID-19. To understand the role of making and sharing food in our lives over the pandemic, I look to my father’s childhood experiences of the Great Depression of the 1930s as a counterpoint. These intergenerational connections reveal ways in which comfort food anchors us at the same time it shapes our aspirations by allowing us to imagine our future selves.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".