Bibliographic record
Abstract
I apply political ecologist Ryan Galt’s concept of ‘subversive and interstitial food spaces’ (Galt et al., 2014, 133) to read Chinese American writer Ava Chin’s semi-autobiographical memoir, Eating Wildly (2014), and Chinese Canadian writer Rita Wong’s poem collection, forage (2007). Beyond offering a different cultural perspective, I argue that Chin’s and Wong’s urban foraging narratives can be read as transitioning from being interstitial to subversive in the North American context. I see urban spaces where plants are foraged but not normally considered to be cultivatable as interstitial. Analogously, I regard people situated between cultures or on the margins of dominant spaces due to their race or class as being in an interstitial position. Echoing ancient East Asian and specifically Chinese environmental thinking, which is relational, non-linear, and non-dichotomous, Chin’s and Wong’s foraging discourses in their poetic, eth(n)ic, and environmental complexities challenge dominant white foraging narratives and provide alternatives to mainstream environmental thinking. Both urban foraging experiences depicted in Eating Wildly and forage thrive from interstitial spatiality, yet they direct us toward subversive and sustainable foodways that promotes food justice and dismantles rural-urban, local-global, human-nature binaries. I will also highlight how the two authors differ in their foraging poetics and politics.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".