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.
Bibliographic record
Abstract
Abstract This paper presents a new pattern discovery method for labelled folk song corpora. The method discovers general patterns that are rare or even entirely absent from a set of pieces, and among those the patterns that are frequent in a background set. Pattern discovery is performed with reference to a background ontology of folk tune genres. The method is applied to a large corpus of Basque folk tunes and results are evaluated as descriptive patterns and as negative association rules. Acknowledgments The Fundación Euskomedia and Fundación Eresbil are graciously thanked for participating in the project and providing the Cancionero Vasco for study. This research was partially supported by a grant Análisis Computacional de la Música Folclórica Vasca (2011–2012) from the Diputación Foral de Gipuzkoa, Spain. Thanks to Izaro Goienetxea for assistance with ontology building and pattern interpretation. Special thanks to Kerstin Neubarth and the reviewers for valuable comments on the manuscript. Notes Darrell Conklin, Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, San Sebastián, Spain, and IKERBASQUE, Basque Foundation for Science, Bilbao, Spain. www.euskomedia.org www.eresbil.com
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it