Visualizing community pride: engaging community through photo- and video-voice methods
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.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".