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Record W2568358222 · doi:10.18061/emr.v11i2.5475

Commentary on Ohriner (2016)

2017· article· en· W2568358222 on OpenAlexaff
Nathaniel Condit-Schultz

Bibliographic record

VenueEmpirical Musicology Review · 2017
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsMcGill University
Fundersnot available
KeywordsPresentation (obstetrics)MusicalComputer scienceMusical analysisSampling (signal processing)Transcription (linguistics)SegmentationCognitive sciencePsychologyData scienceEpistemologyLinguisticsCognitive psychologyArtificial intelligenceArtVisual artsPhilosophy

Abstract

fetched live from OpenAlex

This commentary offers a review of Ohriner's corpus-based research on rap music, and includes an in-depth comparison of Ohriner's model with my own. It highlights multiple divergences in our underlying methodologies, and outlines the potential impact of such differences on the research results. Ohriner provides a convincing and widely appealing presentation of how musical corpora may be leveraged to effectively enhance traditional forms of analysis. However, I question his approach to certain processes, such as sampling, transcription, and segmentation. I believe that his analysis would benefit from stronger definitions in these areas.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.140
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.091
GPT teacher head0.379
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

Classification

machine, unvalidated

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

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2017
Admission routes1
Has abstractyes

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