International perspectives on pedagogies of inclusion in music education: Theorising practice
Bibliographic record
Abstract
Music education needs to move beyond a fragmented and superficial treatment of diversity. We live in a world where issues of achievement, social equality, learner disaffection and teaching strategies are issues which are rightly at the forefront of educational issues. Despite this, music teachers working with disaffected learners in areas where there is risk of social exclusion are confronted with difficulties: most of them have not been professionally trained for the present situation and lack materials, methods and support for reflection. This Symposium builds on the growing interest in this area . A central tenet of the issue is that if researchers are to understand and characterise pedagogies of inclusion in the context of music education, they need to examine the nature and influence of the interactions, relationships and cultures which constitute and sustain such activity. We also need to move thinking forward on what constitutes effective pedagogies of inclusion undertaken in music education of relevance to researchers and practitioners in inclusion in music and across other domains. In this symposium, authors from around the world (including Sweden, Australia, Hong Kong, the UK, the USA and Canada) each offer a distinctive and valuable contribution to this growing field of scholarship, presenting a wide variety of perspectives, experiences and pedagogical approaches relating to inclusive practice in music education. The aim of this roundtable discussion is thus to: i.explore the complex interplay of culture, values and music pedagogy on issues relating to social inclusion, prompting further discussion on what is universal in inclusive music education and what is country-specific or culture-bound; ii.shed light on distinctive pedagogies in music education that aim to ‘include’ those students who would by otherwise excluded; and iii.pose questions relating to ways that individual teachers can learn, borrow or adapt pedagogies developed in accordance with the needs of the students. At the end of this symposium, audience members will be encouraged join the discussion on pedagogies of inclusion, sharing their own experiences and highlighting future areas for research in this growing area.
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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 | 0.031 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.013 | 0.097 |
| Scholarly communication | 0.033 | 0.038 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".