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Record W3096909223 · doi:10.1177/0305735620964315

Strategic approaches to learning sight-singing at college level: An investigation using Q Methodology

2020· article· en· W3096909223 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychology of Music · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicQ Methodology Applications
Canadian institutionsSimon Fraser UniversityUniversité Laval
Fundersnot available
KeywordsSingingSightTypologyPsychologyMusicalMusic educationMathematics educationPedagogyVisual artsSociologyManagement

Abstract

fetched live from OpenAlex

This research investigates the main strategic approaches used by students for learning sight-singing in aural skills training. Using Q method, 41 college-level music students were invited to think about the importance of a wide range of strategies for their sight-singing acquisition. Factor analysis revealed three main strategic approaches: the pragmatic approach, the analytic approach, and the sound-first approach. Post hoc analyses indicated that these strategic approaches do not provide a valid typology of music students; rather, they reveal underlying conceptions about the purposes of sight-singing, which are likely to evolve according to an individual’s musical training. For sight-singing strategy instruction, these findings offer new insights for understanding better the influence of students’ prior musical knowledge on their use of sight-singing strategies. The discussion highlights the need for (re)establishing clear educational expectations that are capable of fulfilling teachers’ musical ideals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.952
GPT teacher head0.541
Teacher spread0.411 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it