Bibliographic record
Abstract
The pop/alternative musician Beck created a stir in the music world when he released his 2012 “album” Song Reader as a book compilation of individual pieces of sheet music. This included a guide to reading music notation, together with an introduction describing the work’s intentions and inviting readers to perform their own versions of the songs. Two years later, a recording of the songs interpreted by various well-known artists was issued. The video to one of these, Jack White’s interpretation of “I’m Down,” focuses entirely on the presentation of the track in the book-album Song Reader: the musical notation, lyrics, and artwork. Using multimodal discourse analysis together with Derrida’s notion of grammatology, this article will analyse both the book-album and the “I’m Down” video. If the Derridean violence of writing brings speech under its over-arching wings, then notation can be seen as dominating musical discourse: the habit of not notating popular music is in fact a (usually subconscious) semiotic decision to differentiate from the (classical/art) music tradition. Song Reader’s release as a book, only to be later reappropriated as a “normal” album, means that it can be understood as an example of “unbound” popular music reincorporated into the mainstream—yet the “I’m Down” video can be read (literally) as a rebellion against this. Or can it? Without their “alternative mainstream” status, neither Beck nor White would have been able to exploit the popular music business in this way. To what extent can the institutional discourse of popular music be infiltrated from the inside? What is the status of “unbound” music-as-literature?
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".