“Out of Time” and “Out of Tune”: Reflections of an Oud Apprentice in Somaliland
Bibliographic record
Abstract
Abstract In this article, I use my oud lessons in Somaliland as a point of departure to reflect on the analytical and methodological potential of musical apprenticeship, with a particular focus on what and how we might learn from playing “out of time” and “out of tune.” Organized around three lessons, each beginning in a moment of “mistuning,” I reflect both on what it means to become the kind of person who can make music in a religiously contested postwar context and on the process of mistake-making, by which apprentices come into new knowledge about music-making and musical personhood. Qoraalkan wuxuu ku saabsan yahay casharradaydii kabanka ee Somaliland. Waxaan halkan ku qeexayaa habka iyo falanqaynta barashada muusigga, gaar ahaan waxaan idiin hogatusaalayn doonaa qaabka looga baran karo codka jaban iyo laaxinka. Waxaan qaadaadhigayaa saddex cashar oo mid walba ku furmayo cod doorsamay iyo laaxin. Anoo isticmaalaya khaladadaydii barashada kabanka, waxaan dib u eegayaa sida uu ku hirgalo muusigistaha ku barbaarey xaalad gaar aa oo ka dhalataye hirdanka ka dhexeeya diinta iyo fanka dagaalladii sokeeye ka dib ee Somaliland. Waxaan sidoo kale falanqaynayaa hannaanka kufa-kaca ah ee qofka gacan yaraha ahi uu ku kasboda aqoon cusub oo qusaya curinta muusigga iyo kaalintiisa muusigyahannimo.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.020 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".