Bibliographic record
Abstract
Africa has nurtured a surprising array of modern and neo-traditional guitar styles. Portable, rugged, versatile, and relatively easy to construct, the guitar has thrived in African settings to the point where today it is among the most pervasive instruments continent-wide, second only to the drum. In places like Mali and Madagascar, ancient instrumental traditions have inspired distinctive acoustic guitar finger-picking techniques. Elsewhere – in Zimbabwe, Guinea, and Cameroon, for example – pre-guitar traditions have evolved into guitar-based, electric “afropop,” once again engendering techniques and sounds unique in world music. In Congo in the 1950s, bands trying to play Cuban dance music substituted the handy guitar for the more rare piano, and within a few years, they developed a highly influential method of layering multiple electric guitar lines. Looking at the range and diversity of guitar innovations in Africa, one could argue that only rock and roll has so revolutionized the instrument over the course of the twentieth century. African guitarists are now beginning to earn widespread recognition, and their work is sure to have further impact around the world in years to come. Consider these recent developments. Paul Simon drew upon guitarists Ray Phiri of South Africa and Vincent Nguini of Cameroon while creating the music for his Grammy Award-winning Graceland project. Ry Cooder also won a Grammy Award in 1994 for his collaboration with northern Malian guitarist Ali Farka Touré, TalkingTimbuktu (WorldCircuit/Rykodisc 1994). That release focused attention on the connections between blues and Malian music and opened the door for other “Malian bluesmen,” such as guitarist and singer Lobi Traoré.
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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 | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".