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Record W7094545278

Homelessness & Activism in Toronto & Montreal: Toward Community-Based Participatory Research & Emergent Strategy

2024· other· en· W7094545278 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2024
Typeother
Languageen
FieldMathematics
TopicMathematics Education and Programs
Canadian institutionsnot available
Fundersnot available
KeywordsParticipatory action researchInterviewNonprobability samplingCommunity-based participatory researchCitizen journalismPerspective (graphical)Participant observationQualitative researchEliteCommunity organizing
DOInot available

Abstract

fetched live from OpenAlex

This research explores generating a community-based participatory research (CBPR) project on technology, broadly construed, with housing justice and anti-poverty activist communities in Canada. This research employs stratified purposive sampling and semi-structured open-ended interviewing methods with Toronto and Montréal activists and is approached from the perspective of a CBPR methodology. 12 activists were interviewed for this research. Interviews indicate that barriers and access to technology centre on education, trustworthiness, usability, and dependability. However, activists also desire systemic and structural change grounded in communities. Activists indicate that elite academic researchers, people who represent the state, and corporations cannot solve issues surrounding homelessness and the Canadian housing market. Instead, activists recommend emergent project creation anchored in community as a possible avenue for mitigating aspects of these phenomena. As such, this research provides an appropriate foundation for multiple community projects beyond this text.

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)0.007
metaresearch head score (Gemma)0.007
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.522

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0230.016
Scholarly communication0.0070.002
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.248
GPT teacher head0.352
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 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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