Picture This: Exploring Photovoice as a Method to Understand Lived Experiences in Marginal Neighbourhoods
Bibliographic record
Abstract
Scholars in the social sciences are increasingly turning to research questions that explore everyday lived experiences, using participatory visual methodologies to promote critical reflections on urban challenges. In contrast with traditional research approaches, participatory visual methods engage directly with community participants, foregrounding their daily realities, and working towards collaborative knowledge production of participants’ situated experiences, potentially leading to transformative thinking and action. This participatory turn in research intersects with growing interests in community participation in collaborative planning and effective ways of engaging “unheard voices” in a planning context, particularly in marginalized neighbourhoods, using arts-based methods. This article critically examines the potential of participatory visual methodologies, exploring how the method of photovoice can reveal otherwise obscured perspectives from the viewpoint of communities in marginalised neighbourhoods. Based on a case study in the Downtown Eastside, Vancouver, the research considers whether and how creative participatory approaches can contribute to giving voice to communities and, if so, how these methods can impact a city’s planning for urban futures. The research shows that, potentially, photovoice can provide a means of communicating community perspectives, reimagining place within the framework of participatory planning processes to those who make decisions on the neighbourhood’s future. However, the research also demonstrates that there are limitations to the approach, bringing into sharp focus the ethical dimensions and challenges of participatory visual methodologies as a tool for engaging with communities, in an urban planning context.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".