<i>Shakespeare’s Early Readers: A Cultural History from 1590 to 1800</i> . By <scp>Jean-Christophe Mayer</scp> <i>Shakespeare's Early Readers: A Cultural History from 1590 to 1800.</i> . By MayerJean-Christophe. Cambridge: Cambridge University Press. 2018. xv + 259 pp. £75. <scp>isbn</scp> 978 1 107 13833 9.
Bibliographic record
Abstract
Richly endowed with examples, Jean-Christophe Mayer's Shakespeare's Early Readers: A Cultural History from 1590 to 1800 is a strong addition to the quickly growing literature of ‘readership studies’. Devised according to six chapters with a rigid structuring of subsections—for example, Chapter 1 has 13 subdivisions— Mayer's survey of the ‘rise of the reader’ contributes to the field by finding a logical and entirely helpful order to elaborate on mostly original findings. The first chapter skilfully cherry-picks from different studies to reimagine the literacy and economic conditions of ‘early buyers and collectors of Shakespeare’. It updates and expands on Alan H. Nelson, ‘Shakespeare and the Bibliophiles: From the Earliest Years to 1616’, in Owners, Annotators and the Signs of Reading, edited by Robin Myers, Michael Harris, and Giles Mandelbrote (London, 2005), pp. 49–73. Chapter 2 maps the ‘material world’ of Shakespeare's early readers from objects and the impression of objects found...
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".