“Nothing says gentrification like being able to order a cortado”
Bibliographic record
Abstract
Alison Hope Alkon, Yuki Kato, and Joshua Sbicca are the co-editors of A Recipe for Gentrification: Food, Power, and Resistance in the City (NYU Press, 2020). This important new book outlines the ways that food and gentrification are closely intertwined in North American cities—not just in New York City or Vancouver, British Columbia, but in smaller, less obvious places like Portland, Oregon; Oklahoma City, Oklahoma; and Durham, North Carolina. A Recipe for Gentrification makes clear that gentrification processes are both complex and contradictory, combining delicious foods with deep feelings of discomfort as vulnerable communities become even more vulnerable due to rising rents and urban displacement. New opportunities for restaurateurs and diners do not necessarily translate into fair wages, shared profits, or dignified living conditions for residents. This research gives us new tools to critically appraise how changing urban foodscapes can engender displacement but also resistance.Two food scholars and fans of this book, Josée Johnston and Michael Chrobok, were delighted to have the chance to sit down and talk with Alison (AA), Yuki (YK), and Joshua (JS) about their new volume—as well as their broader thoughts on food and gentrification, including beyond the North American urban context. The interview has been lightly edited for clarity and length.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".