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Record W2773822998 · doi:10.1177/0305735617745149

Cognitive strategies in sight-singing: The development of an inventory for aural skills pedagogy

2017· article· en· W2773822998 on OpenAlexaff
Guillaume Fournier, María Teresa Moreno Sala, Francis Dubé, Susan O’Neill

Bibliographic record

VenuePsychology of Music · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsSimon Fraser UniversityUniversité Laval
Fundersnot available
KeywordsSingingPsychologyCategorizationCognitionReading (process)SightVocabularyCognitive psychologyThematic analysisActive listeningCommunicationQualitative researchLinguistics

Abstract

fetched live from OpenAlex

This research aimed to identify, describe and categorize cognitive strategies related to sight-singing within aural skills education. Using a constant comparative method, we carried out a thematic content analysis using NVivo to categorize strategies in a broad range of sources, including six interviews, five scientific publications, two professional books, and two ear-training manuals. Findings revealed 72 cognitive strategies grouped into four main categories and 14 subcategories: reading mechanisms (pitch decoding, pattern building, validation), sight-singing (preparation, performance), reading skills acquisition (musical vocabulary enrichment, symbolic associations, internalization, rehearsal techniques) and learning support (self-regulation, attention, time management, motivation, stress). Our cognitive strategy inventory provides a new framework for the study of cognitive strategies in aural skills research, and offers new insights for teachers who implement explicit cognitive strategies within their sight-singing pedagogy.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
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 score0.731
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.149
GPT teacher head0.434
Teacher spread0.285 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2017
Admission routes1
Has abstractyes

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