DEVELOPMENTS IN MUSIC TECHNOLOGY: HYBRID ACTIVITY IN POPULAR MUSIC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it