Bibliographic record
Abstract
This article advances the concept of the agroecological “lighthouse” as a civic space for learning and participating in the principles and practices of urban food production. As urbanization threatens to encourage the increased industrialization of agriculture, growing food in cities promises to alleviate this pressure while creating new opportunities for community empowerment and greater access to sustainable, healthy, and affordable food. This kind of transition, I argue, will demand social relations that bridge science, practice, and movement—and that cut in surprising ways across traditional boundaries between university and community. Drawing from a recent experience in an Urban Agroecology shortcourse in Berkeley, California, I illustrate what such relationships might look like, profiling the caretaker of one backyard garden in the Bay Area. This urban grower effuses what James Scott calls metis, moving fluidly across institutional boundaries, experimenting with agroecological innovations, and offering his space as a lighthouse commons for participatory learning. Interestingly, he is not a PhD, but a retired postal worker. With the stakes mounting for progress in food security across the urban-rural divide, the agroecological lighthouse opens up potential for new researcher-farmer partnerships as well as a means for expanding what we consider legitimate knowledge-making communities. Advancing the notion of a “lighthouse extension model,” I challenge the discourse of mainstream cooperative extension, arguing that a more egalitarian food system will likely emerge from participation by those traditionally excluded from shaping it.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".