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Record W3108266917 · doi:10.31542/muse.v4i1.1956

Lennon vs. McCartney

2020· article· en· W3108266917 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueMacEwan University Student eJournal · 2020
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsMacEwan University
Fundersnot available
KeywordsChord (peer-to-peer)Markov chainVariety (cybernetics)LiteratureComputer scienceArtSpeech recognitionArtificial intelligenceMachine learning

Abstract

fetched live from OpenAlex

In this analysis, the chord progressions used in songs by the Beatles are modelled as Markov chains to identify potential differences between songs for which John Lennon had more influence and those for which Paul McCartney had more influence. A preliminary comparison of random samples of songs from each artist did not identify noteworthy differences between Lennon and McCartney; most pieces resulted in regular Markov chains. This analysis then focusses on two songs from the Beatles – “Norwegian Wood”, primarily written by John Lennon, and “Good Day Sunshine”, primarily written by Paul McCartney – which deviated from this pattern. Similar patterns were found between the two songs despite major differences in the chords that made up each state space. In general, however, McCartney’s song had more variety in terms of the number of chords used and the paths taken between tonic chords.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score0.451

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.019
GPT teacher head0.218
Teacher spread0.198 · 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