How might photovoice be used in a digital era? \nCollaborating with rural youth to explore environmental pedagogy
Bibliographic record
Abstract
This thesis examines how photovoice as a methodology can be used in a digital era to connect rural youth with their environment. To give agency to rural youth and empower the participants in our environmental crisis, I developed a photovoice workshop to foster a caring relationship with nature by anchoring my approach in environment pedagogy, place-based learning and Paulo Freire's theory of critical-action, critical-consciousness, and active learning. Using an adaptation of Caroline Wang and Mary Ann Burris' photovoice methodology, I adopted Aboulkacem et al.'s (2021) photovoice 2.0 method to address digital photography and the necessity for visual literacy. I facilitated a photovoice workshop for Grades 5 to 8 at a small community two hours north-west of Montreal, Quebec. The workshop was a 6-week extra-curricular activity held in the school library twice a week during the participant's 55-minute lunch break. The workshop's theme was the environment as we explored the forest behind the participant's school to engage in an eco-responsible relationship through photography and group discussions. The outcome was a group exhibit installed in the participant's school, where they invited the local and school community to attend their vernissage. \n \nKeywords: photovoice, environment, photography, visual literacy
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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 | 0.005 | 0.005 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".