Bibliographic record
Abstract
“Metaphor is a poor metaphor for what needs to be done.” (Eve, 2024, p. 10) Martin Paul Eve’s (2024) Theses on the Metaphors of Digital–Textual History aligns with a tradition of meta-critical studies of digital tools in the humanities—why the tools we use in the humanities matter for them. This includes, for example, Kirschenbaum’s (2012, 2021) Mechanisms: New Media and the Forensic Imagination and Bitstreams: The Future of Digital Literary Heritage, both of which Eve (more or less explicitly) identifies as central influences. The book also belongs to a larger set of publications by Martin Paul Eve (2014a,b, 2019, 2021) that includes, for instance, Warez, Close Reading with Computers, Open Access and the Humanities, and Pynchon and Philosophy. All these works similarly interrogate the epistemic conditions of reading, writing, and scholarship in the digital age. Each of these books engages, in different ways, with how “frames”—computational, infrastructural, institutional, or philosophical—shape scholarly inquiry. Theses synthesizes Eve’s meta-critical impulse by focusing on metaphorical referentiality as a structuring principle across digital and textual domains. In a sense, it is the most distilled and conceptual of Eve’s works, offering a portable critique of the metaphors that underpin all the others.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 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".