<i>Abstractions of Evidence in the Study of Manuscripts and Early Printed Books</i> . By J <scp>oseph</scp> A. D <scp>ane</scp> . <i>Abstractions of Evidence in the Study of Manuscripts and Early Printed Books</i> . By DaneJoseph A.. Farnham: Ashgate. 2009. viii + 176 pp. £55. <scp>isbn</scp> 978 0 7546 6501 4.
Bibliographic record
Abstract
Joseph Dane’sThe Myth of Print Culture (2003) asked us to think about a major problem of critical practice in bibliography: the relationship between the evidence studied by scholars of the book and the framing assumptions they necessarily bring to bear on those books in order to study them. Forcefully and with considerable wit he argued that ‘the gap between material and textual levels in bibliographical discussion is one that can never be closed, and it is one that scholarship, in its own advance, discovers new and more mystifying ways to obscure’ (pp. 3–4). Nominalist in argument, The Myth of Print Culture was also nominalist in form: a book of sepa rate essays, profoundly and usually amusingly sceptical. Reviewing it (in The Library, vii, 5 (2004), 322–25) I wrote that ‘if we accept Dane’s assertion that philoso phical principles are at issue […] we are entitled to ask if the principles he asserts are adequate. […] Dane has difficulty admitting the power of hypothesis to search out truth, because he seems to assume that all hypotheses, and with them all general explanations, are kinds of absolutism.’
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".