The Archives of Achab: the exhaustive and the elusive in the digital rewritings of Moby Dick
Bibliographic record
Abstract
Matt Kish’s Moby Dick in Pictures (2011) and Fred Benenson’s Emoji Dick (2010) follow the path of an incalculable number of reworks based on Herman Melville’s classic. The first one introduces a pictorial rewriting of Melville’s novel, where each and every page of the book is being replaced by a corresponding illustration, whereas the second one offers a semi-automated participatory translation of the original text, relying exclusively on the use of emojis. Our goal in this article will be to identify how those works both echo and comment on issues from the initial text, while remaining deeply anchored in a digital aesthetic and practice. Focusing on the matters of the exhaustiveness and the unseizability striking the bodies of both the whale and the text itself, we will see how both Kish and Benenson involve Melvillean themes, while formulating, through the specificities of their respective convocation, a reflection on the task of intertextual rework in a digital context.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".