<i>Bound to Read: Compilations, Collections, and the Making of Renaissance Literature</i> . By J <scp>effrey</scp> T <scp>odd</scp> K <scp>night</scp> . <i>Bound to Read: Compilations, Collections, and the Making of Renaissance Literature</i> . By KnightJeffrey Todd. Philadelphia: University of Pennsylvania Press. 2013. [viii] + 279 pp. £39. <scp>isbn</scp> 978 0 8122 4507 3.
Bibliographic record
Abstract
‘How we collate is how we think’ writes Jeffrey Todd Knight near the end of Bound to Read: Compilations, Collections, and the Making of Renaissance Literature. ‘Collating’ here is somewhat broadly interpreted: Knight's new volume begins among the myriad Renaissance texts and booklets that were issued in small formats and then ‘collated’ (that is, collected) and bound together by or for their owners into miscellaneous volumes that would survive on the shelf longer than a single casual pamphlet. The result is the sammelband, a format anyone frequenting libraries well-stocked with books from the period before 1800 will have encountered: tract volumes, collections of plays or political pamphlets, miscellaneous writings bound together for little reason other than that they were roughly the same size. Knight is interested, however, not only in how such texts were brought together but also in printed books and personal manuscripts that were recompiled, interleaved, and annotated by their authors. ‘For generations of collectors and owners whose legacy is still visible in archives,’ he writes, ‘the relatively flexible composite volume was the most conventional, practical means of storing and using most kinds of literary texts’ (p. 61).
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.184 | 0.073 |
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".