Photovoice and House Meetings Within Participatory Action Research
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".