DEVELOPMENTS IN MUSIC TECHNOLOGY: HYBRID ACTIVITY IN POPULAR MUSIC
Bibliographic record
Abstract
The most critical [issues] to which we should turn our attention are those that have consequences for the movement of music within and through different (and sometimes altogether new) spaces, such as changes in sales mechanisms, Internet broadcasting, the use of computers for producing, consuming and distributing music, and the personalisation of musical tastes and behaviours. (Jones, “Musicand the Internet” 225) Since the invention of recorded sound, music and the technology with which it is recorded have been entwined. From the phonograph to the mp3, the history of popular music production, distribution and consumption in the twentieth century is one marked by various technological innovations (see for example Coleman, 2003; Garofalo, 1999). Currently, new digital recording technologies are facilitating changes to the music making process (Théberge, 1997). Sophisticated software programs such as ProTools and Nuendo offer near-professional song recording, mixing and mastering abilities while Reason, Acid, plus a host of other programs encourage the manipulation of original or sample-based sounds. Innovations in the technologies of consumption are causing similar impacts to the listening process (Bull, 2000). Digital jukeboxes, mp3 players and new business models from the likes of iTunes and Napster 2.0 are affecting the way we receive and use music. In many ways, the processes associated with production and consumption are currently converging into one machine: the computer.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".