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Record W7009273641

Displacement City: Fighting for Health and Homes in a Pandemic

2024· article· en· W7009273641 on OpenAlexaboutno aff

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicProsperityLitanyAction (physics)Metropolitan areaStyle (visual arts)Displacement (psychology)OutreachMental health
DOInot available

Abstract

fetched live from OpenAlex

Book Review of "Displacement City: Fighting for Health and Homes in a Pandemic." Coedited by outreach worker Greg Cook and street nurse Cathy Crowe, Displacement City is an anthology of more than twenty authors contributing prose, poetry, and photography. It explores the lived experiences of homelessness and the efforts of frontline services in Toronto during the COVID-19 pandemic. Divided into three parts, the book champions collective action from tenacious yet exasperated community organizers against an alarming backdrop of inadequate responses by city authorities to provide for those without a home. Predominantly written in an emotionally descriptive style of firsthand accounts, with testimonies of anger, activism, and advocacy, Displacement City is personal and persuasive in tone. It begins with a litany of deaths in the unhoused community in Toronto, as a memoriam for prosperity but also a sharp reminder of how the pandemic was suffered inequitably. This sets the scene for a city simultaneously failing to address the triple disaster of a worsening opioid crisis, an escalating homelessness emergency, and a spreading virus. (cont.)

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.121
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0300.008

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 head0.027
GPT teacher head0.299
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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