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Record W2604194378 · doi:10.1177/1476750317695411

Using neighborhood observation to support public housing tenants’ empowerment

2017· article· en· W2604194378 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAction Research · 2017
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité de SherbrookeUniversité du Québec à MontréalWilfrid Laurier University
Fundersnot available
KeywordsPhotovoiceEmpowermentParticipatory action researchSociologyAction researchAction (physics)Public relationsPhase (matter)Citizen journalismExperiential learningKnowledge managementComputer sciencePolitical sciencePedagogyEconomic growth

Abstract

fetched live from OpenAlex

Although public housing is often described as a negative and stigmatized environment, tenants living in such an environment can cultivate a positive sense of community, which enhances their individual and collective well-being. The present study describes the second phase of an action research, aiming to facilitate the empowerment of public housing tenants acting as peer-researchers. Following a Photovoice phase, this second phase focuses on the development and first implementation of a participatory observation method as a tool for evaluating tenants' collective environment fit. A group of nine tenants contributed to develop and later completed an observation grid. The observations were then discussed in decision-making sessions. The participatory observation method proved useful in supporting tenants in their reflection process, promoting the depiction of a nuanced portrait of their residential environment while also prioritizing capacity building. Results are currently used to inform an action phase in which tenants are taking increasingly more power. Triangulating the results from multiple sites is needed to establish more firmly the added-value of this observation method in a larger action research project. Key challenges and lessons learned are described in a reflective section, sharing experiential knowledge with researchers that consider using a similar method.

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.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0110.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.861
GPT teacher head0.676
Teacher spread0.185 · 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