<i>Format Friction: Perspectives on the Shellac Disc</i> . By Gavin Williams
Bibliographic record
Abstract
Wax, wood, tin, skin, mica, ivory, rubber, paper, plastic—and shellac, of course, and much else. Take your pick. It is now possible to read about music through the protagonistic lens of any thing you can imagine. If this kind of character study is having a moment in musical thought and scholarship, it is a genre unto itself in creative non-fiction and microhistory and certain kinds of anthropology. Consider John McPhee’s Oranges, for example. This unmatched early specimen of the genre, from 1966, offers a bookful of sweet facts about oranges concentrated into a juicy story about citrus fruit. To know oranges, it turns out, is to know our world—and to know ourselves. Since the time of Oranges, it has only become clearer just how revealing are the consumables and durables of existence when it comes to the human condition. From aspirin to zippers and everything between, McPhee and his ilk have seen the world in a grain of salt, and a heaven in a hamburger.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".