PHOTOVOICE METHOD TRENDS, STATUS AND POTENTIAL FOR FUTURE PARTICIPATORY RESEARCH APPROACH
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
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.
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 | 0.030 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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