The Case of the Missing Author: Toward an Anatomy of Collaboration in Comics
Bibliographic record
Abstract
In a January 2012 interview for BBC4, Matthew Cain questions the painter David Hockney on his use of assistants. Cain is trying to get at whether Hockney’s three assistants produce any of his art. But Hockney doesn’t bite. He says that he made “all the marks” and that an assistant would never pick up a paintbrush. The role of the assistant is purely that of the logistical helper; he or she does physical work, but not the work most associated with the production of capital “A” art: the relationship of the hand, the eye, and the heart. David Hockney is an art star, significant as both commercial brand and artist. His name confers value to his artwork. An anonymous painting that looked like a David Hockney piece, but wasn’t, would be dismissed as either a valueless imitation or a forgery. Even people who cannot afford a David Hockney work have some stake in its authenticity, otherwise interviewers like Matthew Cain would not ask questions about it. It is a relief that Hockney “made all the marks.” These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".