MétaCan
Menu
Back to cohort
Record W1574238033 · doi:10.22329/celt.v2i0.3210

16. Using Content-Specific Lyrics to Familiar Tunes in a Large Lecture Setting

2009· article· en· W1574238033 on OpenAlex
Derek T. McLachlin

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

VenueCollected Essays on Learning and Teaching · 2009
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsWestern University
Fundersnot available
KeywordsLyricsPsychologyTheme (computing)Set (abstract data type)Class (philosophy)Content (measure theory)Content analysisSelection (genetic algorithm)PerceptionMathematics educationPedagogyLiteratureArtComputer scienceSociologySocial science

Abstract

fetched live from OpenAlex

Music can be used in lectures to increase student engagement and help students retain information. In this paper, I describe my use of biochemistry-related lyrics written to the tune of the theme to the television show, The Flintstones, in a large class setting (400-800 students). To determine student perceptions, the class was surveyed several weeks after the song was used. Students reported a high level of engagement and enjoyment during the song. Many students found the song to be a helpful study tool. To guide future song selection, the students were also asked to indicate their familiarity with 30 popular songs from the past 50+ years. The songs that were least familiar to the students were all released before 1980, but some older songs were well known. The results support the use of content-specific lyrics set to familiar tunes as an educational tool, and provides information about specific songs that would or would not be suitable for this purpose.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.262
Teacher spread0.240 · 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