Examining the Musical Identity of Pre-service Generalist Teachers: Origins and Implications
Bibliographic record
Abstract
In this presentation, an attempt is made to investigate the ways of thinking and knowing about the ‘practice’ of music by generalist teachers using the parameters described by Jorgensen (2008) such as; tradition, values, dispositions and attitudes. This presentation is a report on the results of a study of generalist music preservice teachers conducted at The University of Western Ontario, Faculty of Education. The unveiling of the ‘practice’ of music in this population as their identities are in flux may reveal ways of knowing and understanding ourselves in society. The field of music education may gain a Meta perspective (Johansen, 2010) of itself through a sociological lens, in essence, making the familiar become strange (Wright, 2010). While contemporary literature and resources available support the preservice music specialist, there is very little focus from the field of music education towards the preservice generalist music teacher. Advocacy for specialist teachers is quite successful in many school systems; however, there are many more schools without adequate funding for specialist teachers. In 2005, indications show that there are instances in Ontario where 70% of the elementary school children are receiving music instruction from a non-specialist (Montgomery & Griffin, 2005). This leaves the generalist teacher to design and implement their own music program, or omit it from the curriculum entirely. In this study we wish to illuminate issues of identity and attitudes towards music teaching from non-specialists, and to address the origins of their beliefs and attitudes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".