Breaking Down Barriers: Exploring the Potential of Participatory Visual Research to Promote University Students' Active Participation in Sustainable Development Initiatives
Bibliographic record
Abstract
Education for Sustainable Development (ESD) serves as one of the most promising approaches amidst the current global sustainability crisis. Universities, as research and innovation hubs, have the critical responsibility to make graduates competent as sustainable citizens. Still, research shows that the rigid, top-down approaches in institutions often inhibit students' meaningful participation in leadership and decisionmaking opportunities within ESD programs. In this paper, I advocate for using creative participatory methodologies, such as participatory visual research (PVR), to promote students' meaningful participation in sustainability initiatives. Through the review of extant ESD scholarship, I establish that PVR can engage students' participation in sustainability initiatives while promoting their critical awareness of sustainability challenges as well as revealing their implicit and hidden sustainability perceptions and values. This paper provides valuable insights for researchers seeking to leverage PVR's full potential while considering its ethical, practical, and theoretical implications for ESD research.
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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 | 0.115 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".