Visualizing community pride: engaging community through photo- and video-voice methods
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.
Bibliographic record
Abstract
Purpose The purpose of this paper is to consider the potential of visual (i.e. non-textual) research methods in community-based participatory research. Design/methodology/approach The authors draw on a case illustration of a photo- and video-voice campaign involving rural communities in British Columbia, Canada. Findings The authors find that visual research methods, in the form of photo- and video-voice campaigns, allow participants to form ties between their community and the broader sociocultural, natural and political milieu in which their community is located. The authors highlight the benefits of using such methodological approaches to capture an emic perspective of community building. Originality/value The contribution of this study is twofold. First, this study uses a photo- and video-voice campaign to showcase the role of visuals in articulating community pride – that is, how locals construct identity – and a sense of belongingness. Second, by focusing its analytical gaze on the idea of “community,” this paper revisits the importance of active involvement of research participants in the execution of empirical studies. Ultimately, the authors urge organization and management studies scholars, as well as those working in the social sciences more broadly, to further explore the value of innovative community-based research approaches in future work.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.071 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it