City of sanctuary: Exploring multispecies democracy in a post-growth food future
Bibliographic record
Abstract
This article grapples with two pressing problems endemic to capitalist agriculture: the exploitation of nonhuman animals and the enclosure of the agricultural commons. How might a post-growth food transition support animal liberation and restore access to the means of food production? Care farming, an international paradigm integrating agriculture and healthcare through the therapeutic use of farming, may offer a path forward. Employing speculative ethnography, the article explores an expanded and more inclusive political ecology of care farming. It argues, through three fictional vignettes, that care farming and animal rights can resolve practical challenges for one another. The cultural and legal recognition of domesticated animals as members of society is a means for mitigating anthropocentric practices in care farming. Scaling up care farming, in turn, is a way of welcoming domesticated animal citizens into public space and community life. Attending more to social and spatial configurations than technologies, the vignettes hold high- and low-tech approaches to agriculture, interspecies communication, and democratic deliberation in tension. The article concludes by reflecting on questions for further consideration.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.047 | 0.035 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".