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Record W3188031716 · doi:10.1080/11745398.2021.1949735

Where life and leisure intersect: exploring the outdoors as a site of contradictory experiences for person’s living in poverty

2021· article· en· W3188031716 on OpenAlexaffabout
Teresa J. Hill

Bibliographic record

VenueAnnals of Leisure Research · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSociologyPovertyCriminalizationConceptualizationPublic spaceSpace (punctuation)PoliticsCriminologyGender studiesPolitical scienceLaw

Abstract

fetched live from OpenAlex

For individuals experiencing poverty and homelessness, acting out their lives in public spaces can be complicated, as their very existence might be viewed as a transgression of a spaces’ conceptualization [Lefebvre, H. 1991. The Production of Space. London: Blackwell.]. Within this paper, through the work of Henri Lefebvre and Don Mitchell, I examine the ways in which representations of public outdoor spaces in cities impact the lived experiences of those who engage with the sites as a means of survival. Through this work, I argue that the right to be is reliant on an individual’s ability to acceptably (re)produce spaces as they were conceived, or to otherwise be forced to exist in marginal spaces [Mitchell, D., and N. Heynen. 2009. “The Geography of Survival and the Right to the City: Speculation on Surveillance, Legal Innovation, and the Criminalization of Intervention.” Urban Geography 30 (6): 611–632. doi:10.2747/0272-3638.30.6.611; Snow, D., and M. Mulcahy. 2001. “Space, Politics, and the Survival Strategies of the Homeless.” American Behavioral Scientist 45 (1): 149–169. doi:10.1177/00027640121956962]. The empirical insights in this work emerged from nine months of field work at Start Me Up Niagara, a community centre in St. Catharines, Ontario, Canada, which works with people experiencing poverty and homelessness.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.383
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.414
GPT teacher head0.518
Teacher spread0.104 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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