PHOTOVOICE METHOD TRENDS, STATUS AND POTENTIAL FOR FUTURE PARTICIPATORY RESEARCH APPROACH
Bibliographic record
Abstract
In the last decade, researchers from around the world have shown deep interest in using photovoice as a method of analysis in scientific research. This might be due to the participatory strength of the method that acts as a bridge to connect researcher and community by balancing scientific research and mitigating action. The purpose of this research is to synthesize the available research on the photovoice method using the Scientometric method. This article explores the research landscape, key topics, and developments of the photovoice method based on the 1252 document data retrieved from the Web of Science Core Collection dated from 1997 to 2019. The results show that the interest in using this method is significantly high in the United States, Canada, and the United Kingdom as they are the major leaders in publication contributions. A Scientometric analysis for Document co-citation analysis was applied and 15 research clusters were identified. This paper reviews the main characteristics of 6 most important clusters and their contribution to the photovoice method. The outcome of this study contributes to academia, industry practitioners and policymakers by providing an understanding of overall trends, status, and potential research questions of study in this domain.
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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 | 0.210 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".