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Record W4405001009 · doi:10.1515/9780776636429-028

CHAPTER D-4 The Front Line Defence: Housing and Human Rights in the Time of COVID-19

2020· book-chapter· en· W4405001009 on OpenAlexaboutno aff
Leilani Farha, Kaitlin Schwan

Bibliographic record

VenueUniversity of Ottawa Press eBooks · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Front lineHuman rightsFront (military)Line (geometry)Political scienceVirologyGeographyMedicineLawMathematicsGeometryMeteorologyInternal medicine

Abstract

fetched live from OpenAlex

COVID-19 has laid bare the failure of Canadian governments to effectively implement the right to housing.In this chapter, we argue the pandemic presents Canada with the opportunity to correct the structural weaknesses of our housing system to ensure housing for all and reposition housing as a social good rather than a commodity.We explore how housing status has been determinative of outcomes for three vulnerable populations during the pandemic-people experiencing homelessness, survivors of intimate partner violence, and low-income renters.Their experiences demonstrate the urgent need for a rights-based approach to housing, highlighting the importance of breathing life into the National Housing Strategy and the National Housing Strategy Act.We argue that Canadian governments must act before this opportunity passes them by; otherwise they will find that though the pandemic itself is over, housing inequality has only worsened. *Former United Nations Special Rapporteur on the Right to Housing and Global Director of The Shift.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.617
Threshold uncertainty score0.771

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0050.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0380.005

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 head0.047
GPT teacher head0.254
Teacher spread0.208 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2020
Admission routes1
Has abstractyes

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