The Disappearance of Urban Horses and the Rise of Homelessness and Mental Illness
Bibliographic record
Abstract
ABSTRACT Homelessness is growing in cities across the Western world, accompanied by high rates of mental health problems. To address this crisis, programs focus on providing affordable housing and mental health services. Yet this effort seems insufficient to stem the tide. Based on long‐term ethnographic research with horse‐drawn carriage drivers in Montreal, I tell the story of Jerome, who was experiencing homelessness and psychic distress prior to his unexpected encounter with a horse‐drawn carriage. To understand how Jerome reoriented his life, I build on the urban literature on convivial spaces while also drawing on multispecies ethnography's attention to entanglements with non‐human animals. I argue that Jerome benefitted from the “atmosphere of conviviality” of the carriage stand, where horses foster spontaneous interactions, encourage lingering and enjoyment, and facilitate connections across social divides. Encounters in convivial atmospheres may also allow people to build routines and even craft a way of life. This research brings a view of homelessness and mental illness as a process entangled with the urban ecology. The transformation of the city, particularly the disappearance of domestic animals such as horses, may be an overlooked yet significant factor in the rise of unhoused people with psychiatric conditions caught in the institutional circuit.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".