Review Essay: The Representation of Business in English Literature
Bibliographic record
Abstract
An anthology is an odd species of learned text. It is neither the “flesh” of a unified argument produced by a single mind, nor the “fowl” (no pun intended!) of a wide-ranging, multifaceted journal, retooling each month or quarter. Instead, the scholarly anthology offers a set of scholarly essays, heavily footnoted, deep and narrow in focus, and often ambiguously linked. In the case of The Representation of Business in English Literature more than enough intellectual heft is offered, with contributors from some of the United Kingdom’s leading research institutions (Edinburgh, Hull, Leeds—though Oxford and Cambridge are noticeably missing). Notwithstanding, questions of consistency and breadth of focus linger over the volume from start to finish, and at times the evaluation of the literary mind seems to balance on purely mercantile terms, an unfortunate oversimplification that almost swamps the volume—almost. Nonetheless, the essays eventually rally and offer a measure of helpful insight into the world of social critique and exchange of ideas across sometimes-hostile boundaries. Michael R. Stevens, Review Essay: The Representation of Business in English Literature, Journal of Markets & Morality 13, no. 1 (Spring 2010): 205-210
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".