Enrich Competencies for Sustainability through Radio Drama Competitions- A Multiyear Cross-sectional Study in Hong Kong
Bibliographic record
Abstract
This study investigates the use of radio drama competitions to enhance competencies for sustainability among students. It focuses on how radio drama competitions can provide students with opportunities to develop collaboration, critical thinking, and problem-solving competencies within the sustainable development framework proposed by UNESCO. The study utilized a mixed-method approach, including online surveys and in-person interviews, to evaluate the effects of Hong Kong students’ participation in radio drama competitions on the development of their competencies for sustainability over a period of four years. Both the quantitative and qualitative data suggested radio drama competitions had a positive impact on enriching students’ collaboration, critical thinking and problem-solving competencies. Students and teachers believe these critical competencies for sustainability can be acquired and enriched from the radio drama competition. These competencies help develop global citizens who can nurture more sustainable societies (UNESCO, 2017). This research also aims to contribute to the existing body of knowledge by incorporating insights on how radio drama competitions can empower primary and secondary school students from Hong Kong to develop these sustainable competencies through experience and reflection. The radio drama competition offers a platform for students to explore complex sustainability issues, express their creativity, collaborate with other students, as well as engage with a broader audience. In addition, this study provides suggestions on how radio drama competitions can be used as an educational tool for sustainability, integrated into primary or secondary schools’ curriculum, nurture students’ transferrable skills and achieve the goals of Education for Sustainable Development (ESD) at very young age.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".