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Record W2071335018 · doi:10.1525/mp.2009.27.2.89

A Distribution of Absolute Pitch Ability as Revealed by Computerized Testing

2009· article· en· W2071335018 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

VenueMusic Perception An Interdisciplinary Journal · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill UniversityInternational Laboratory for Brain, Music and Sound Research
Fundersnot available
KeywordsRange (aeronautics)Distribution (mathematics)Absolute (philosophy)Test (biology)Computer scienceMathematicsSpeech recognitionStatisticsPsychologyEngineering

Abstract

fetched live from OpenAlex

BEHAVIORAL ASSESSMENT OF ABSOLUTE PITCH (AP) ability over the better part of the last century has strongly suggested that a variety of proficiency levels exists and can only be more comprehensively described with the use of rigorous testing providing precise and unbiased reaction times for all responses. This study describes the design, implementation and validation of a computerized test of absolute pitch and resulting data for 51 musicians, 27 of whom self-reported as AP possessors. The test was sensitive to previously reported differences in accuracy and timing for C major diatonic versus non-diatonic notes and showed a range of performance, from perfect to random, including a substantial number of intermediate levels of proficiency. We discuss the implications of detecting such a distribution of behavior as well as the effect of test design and scoring strategies on that distribution.

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)0.001
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 score0.955
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

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

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 head0.055
GPT teacher head0.342
Teacher spread0.288 · 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