Tunisian Music: The Soundtrack of the Revolution, the Voice of the People
Bibliographic record
Abstract
10 million inhabitants, most of whom I found to be both welcoming and generous. 1 The North borders the Mediterranean and boasts sandy beaches with beautiful aqua blue waters and the capital of Tunis. The South treasures the mysterious and vast Sahara desert and lush oases with their numerous palmeries. It was in this country, 5,000 km from my home here in St. John's, Newfoundland and Labrador, that I found myself at the end of May 2012. I was interested in the opportunity to immerse myself in a part of the world I knew little of, do field research for the first time, talk to first-hand witnesses concerning one of the most important world events of the early second millennia, and delve into the relationship between history and music. These interests culminated in a proposal that I created and submitted to the Summer Research Program at the College of the Holy Cross where I was working on my Bachelors of Arts in French and in Music. I was thrilled to find out that I was accepted to participate in the program sponsored by the Andrew W. Mellon Foundation. As a Mellon Fellow, I spent 5-1/2 weeks conducting research under Holy Cross music professor and librarian Alan Karass. This was Alan's tenth trip to Tunisia as he was working on his Ph.D. in Ethnomusicology on the Douz Festival in the country's south. He
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".