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Record W4414662655 · doi:10.1080/02723638.2025.2561958

A place to find community: the role of autonomy and care in managed encampments

2025· article· en· W4414662655 on OpenAlexaffabout
Laura Pin, Abishane Suthakaran, Nathan Ermeta, Nathan R.G. Barnett

Bibliographic record

VenueUrban Geography · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsAutonomyAgency (philosophy)Government (linguistics)Human geography

Abstract

fetched live from OpenAlex

This paper explores the longest ongoing managed encampment of people experiencing homelessness in Canada: A Better Tent City (ABTC). Located in Kitchener, Ontario, ABTC hosts 48 residents, with another 50 guests and visitors regularly on site. Since ABTC was initiated in 2020, several other managed encampments have emerged in Canada. Yet, as a relatively new policy intervention, little is known about managed encampments as a crisis response to homelessness in Canada. Drawing on survey data, interviews, and personal observations, we apply Orr et al.’s (2024. Beyond revanchism? Learning from sanctioned homeless encampments in the U.S. Urban Geography, 45(3), 433–459) sanctioned encampment typology (SET) to ABTC to understand ABTC’s role in homelessness service provision. We find that ABTC falls into the autonomy/care quadrant of the SET framework, a classification that challenges dominant, hierarchical models of homelessness management. Unlike the more prevalent control/care encampments, ABTC fosters a flexible, relational mode of governance grounded in mutual support, minimal rules, and high resident agency. At the same time, its location on marginal land and reliance on informal relationships underscore the limits of autonomy under insecure tenure. By examining ABTC’s model, we offer refinements to the SET framework and highlight the potential – and constraints – of grassroots, care-oriented encampments to expand conceptions of home and belonging.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.260
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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