Three Examples of Engagement through Photovoice
Bibliographic record
Abstract
Addressing the complex challenges of today’s world requires our collective creative capacity. As such, arts-based methods which promote creativity are increasingly important means of engaging people in the issues that matter most to them. This article focuses on one arts-based method, Photovoice, which is a “process by which people can identify, represent, and enhance their community through a specific photographic technique” (Wang & Burris, 1997, p. 369) where participants take photos in response to a question or topic of inquiry. To explore this engagement method, we draw from the methodological insights gleaned from three Master’s Arts in Leadership capstone projects that employed Photovoice (or variations thereof) as one method of inquiry. The article is organized as follows: We begin by reviewing Photovoice as a research and engagement method and then summarize the three projects, which occurred in two nonprofit organizations and one public sector institution. In the discussion, we then compare and contrast the methodological insights emerging from these projects, including the extent to which each project: (a) enabled workers at various levels of organizational hierarchies to share their voices; (b) required careful attention to ethics; and (c) generated relationships among participants. As this is a methodological paper, our emphasis here is to highlight the process and impact of using Photovoice as a method rather than sharing each of the study findings and conclusions. In each example, Photovoice as both a research and engagement method enabled participants to play a leadership role in participatory engagement, thus deemphasizing top-down decision-making and promoting more integrated approaches to research and leadership as engagement.
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 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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".