Bibliographic record
Abstract
Since 1974, over 60% of the songs included in the Billboard Hot 100 have come from genres where the guitar has a prominent role. This is equivalent to more than 16,000 songs resonating for decades on radio stations, through sound systems, and in cinemas, shaping the U.S. music market and influencing popular culture around the globe. After an initial 25-year period of academic dismissal, many institutions adapted to the growing influence of popular music by introducing various tertiary-level courses for guitarists. Despite this, early observations suggest that the widespread use of staff notation in essential courses—such as those covering harmony, improvisation, theory, composition, and instrumental training—has created several challenges for many guitarists who struggle with sight-reading. Previous studies have noted that sight-reading is especially difficult for guitarists and that they have been labelled as poor sight-readers within academia. However, these studies have largely focused on classical guitar without differentiating it from the popular electric guitar. Surprisingly, neither the variations in the skills required to play each of these instruments nor the differences in the strategies applied to their tuition have been closely examined. This qualitative study aims to gain a pedagogical insight into the formal tuition of staff sight-reading for guitarists playing the electric guitar in genres different from classical music, who are denoted here as popular or modern guitarists. Through interviews and discussions with nine tertiary music tutors working in Australia, Canada, Colombia, England, New Zealand and the USA, this research offers a detailed examination of the role of sight-reading in the academic and professional development of modern guitarists. It also describes common issues encountered in their sight-reading instruction, presents a comprehensive set of strategies and resources used to overcome these challenges, and explores the differences between electric and classical guitar.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".