Me and the world: A methodological exploration of university students’ perspectives on global issues through cellphilms
Bibliographic record
Abstract
University students in the 2020s – Generation Z – have grown up with technology and seen the world shrink around them. They have been exposed to global issues on a scale and with a frequency that is unprecedented. However, except in relation to the climate crisis, there is little in the literature to help understand Gen Z students’ perspectives on global issues. This article reports on a pilot study of visual participatory methods workshops about global issues with 50 students at two universities in Kazakhstan. Students in Kazakhstan have diverse positionalities, which was the starting point to explore their perspectives on global issues. Spatially, they are within the geography of the ex-Soviet space but connected to the world; temporally, they are one of the first generations never to have experienced Soviet rule firsthand yet who continue to live with its imprints. Nevertheless, the issues that most concern Kazakhstani students are similar to those of their global peers: war and conflict, and environmental issues. Students made cellphilms (short informational videos) about pollution, global warming, vandalism, cyber fraud, corruption, ethnic discrimination, and gender inequality. The process of cellphilming helped students think through global issues as they relate to their local environments. Visual participatory methods offer important opportunities for students not only to make sense of global issues but also to contend with the anxieties and concerns that these global issues present. Students’ agency could be increased through using participatory visual methods, responsibly incorporating social media in the classroom, and through open discussion on global issues.
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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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".