ERIC RASMUSSEN and AARON SANTESSO (eds). Comparative Excellence: New Essays on Shakespeare and Johnson.
Bibliographic record
Abstract
The essays in this volume originated as papers delivered at a 2005 symposium held at the University of Nevada when five Shakespeareans and five Johnsonians met to discuss the reciprocal relationship between these two literary greats, exploring not only Shakespeare's influence on Johnson but also Johnson's impact on the reception of Shakespeare. The symposium and the resulting essay collection constitute an innovative attempt at fostering dialogue between scholars of two distinct periods and writers. But what was surely the most interesting aspect of the symposium, the discussion which took place between papers amongst these 10 distinguished speakers of two separate scholarly backgrounds, is lost to us in this volume. The essays seem not to have been revised in any substantial way in order to allude to insights the speakers gained from hearing their colleagues’ papers and, more significantly, the volume lacks an introduction. This would have been an excellent place for the symposium organisers to have elucidated some of the debate that emerged from the delivery of the papers. The essays also seem somewhat haphazardly organised; if there was a methodology behind their ordering, an introduction would have been a useful place to explain this. The overall impression is, therefore, of a somewhat random volume, rather than a systematic new analysis of Shakespeare and Johnson.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".