Bibliographic record
Abstract
Jazz is distinct from certain other art forms – notably Western art music – in its emphasis on performance as the primary medium of creative achievement, and many of the greatest jazz composers, such as Duke Ellington and Thelonious Monk, are also among its greatest players. For this reason, the following discussion of jazz guitar styles appropriately centers on the individual musicians who have pioneered those styles, and, from time to time, on individual recordings that epitomize them. The history of jazz begins in obscurity around the beginning of the twentieth century at a time when much popular and folk music of oral tradition was not yet widely written about or captured on recordings. The guitar was already well entrenched as a versatile instrument for popular music: a “poor man's piano,” maybe, but also a rich resource in its own right. It was one of many stringed instruments, and combinations of one kind or another – including banjo orchestras, mandolin orchestras, Hawaiian groups, Mexican mariachi groups, minstrel groups, and “Gypsy” bands, to name a few – were ubiquitous. Their sound is echoed today in the legacy of folk, bluegrass, and other string-band music, but the range and stand-alone capability of the guitar made it particularly useful for ragtime and blues, the two greatest influences in the formation of jazz.
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 | 0.000 | 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 | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".