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Record W3035270920

Photovoice and House Meetings Within Participatory Action Research

2016· article· en· W3035270920 on OpenAlexfundno aff
Regina Day Langhout, Jesica Siham Fernández, Denise Wyldbore, Jorge Savala

Bibliographic record

VenueScholar Commons (Santa Clara University) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoRadcliffe Institute for Advanced Study, Harvard UniversitySanta Clara UniversityHarvard University
KeywordsPhotovoiceAction (physics)Participatory action researchCitizen journalismSociologyPublic relationsPolitical scienceVisual artsArtAnthropologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Participatory action research (PAR) is an epistemology where community members and researchers collaborate to (a) determine the problem to be researched, (b) collect data, (c) analyze data, (d) come to a conclusion, (e) determine an intervention, (f) implement the intervention, and (g) evaluate the intervention (Fals Borda, 1987). We refer to PAR as an epistemology rather than as a method because most PAR theorists view it as a way for those typically situated outside of science to insert their lived experiences and perspectives into the process of knowledge construction (Fals Borda, 1987). Specifically, PAR allows for the democratization of knowledge production by engaging multiple constituents. Through this PAR process, problem definitions shift, thus posing meaningful implications for community-based interventions and social action that focuses on addressing community members’ needs. Indeed, some argue that PAR is an epistemology that is intimately connected to empowerment and social change (Fals Borda, 1987).\nWe begin our chapter by discussing the two methods within the PAR process, specifically, how photovoice and house meetings work as tools toward social action and empowerment. We highlight some of the relevant literature where these tools have been used. For each method we discuss the steps involved in the process, as well as the benefits and challenges of each. Next, we provide reflections from two of our participant-researchers, who are also coauthors. We end the chapter with implications for community-based PAR and consider how photovoice and house meetings work as tools toward critical consciousness, empowerment, and social action.

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)0.014
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.762
GPT teacher head0.599
Teacher spread0.163 · 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.

Study designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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