Finding common threads: The future of costume pedagogy and practice
Bibliographic record
Abstract
As professional practice and tertiary education face unprecedented challenges, this Special Issue explores the evolving landscape of costume pedagogy. Climate change, social inequity and technological advancements are just some of the issues which are challenging costume educators to create and implement innovative teaching methodologies and approaches. Their focus includes the decolonization of curricula, emphasizing non-western perspectives and re-emphasizing the importance of critical thinking, problem-solving, collaboration and co-creation in educational settings. Additionally, this Special Issue examines efforts to de-gender costume education, reflecting cultural shifts towards more fluid understandings of gendered identity. The integration of digital technologies in costume design is also illuminated as an emerging learning outcome, recognizing the balance between traditional craft skills, embodied awareness and technological proficiency in engaging students. Contributions come from educators across the globe, working in Australia, Canada, Finland, Italy, New Zealand, the United Arab Emirates (UAE), the United States and the United Kingdom, who offer diverse insights and practices aimed at invigorating costume pedagogy. This global reach is emphasized by the inclusion of practices inspired by the costume-related activities at Prague Quadrennial (PQ2023), demonstrating the enduring impact of such international exchanges. This Special Issue presents a snapshot of current trends and future directions in costume education, ultimately advocating for a dynamic, inclusive and responsive educational environment.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".