<i>A Poetics of Editing</i> . By <scp>Susan L. Greenberg</scp> <i>A Poetics of Editing</i> . By GreenbergSusan L.. New York: Palgrave Macmillan. 2018. 265 pp. $120.95. <scp>isbn</scp> 978 3 3199 2245 4.
Bibliographic record
Abstract
In putting more than two words on paper you are bound to experience the editorial moment: the correction and rewriting that is the subject of a cartoon so famous its originator seems untraceable. A figure is busily crossing out and rewriting a wall inscription that says ‘The strongest drive is not Love or Hate [insert image of pencil‐sharpener] It is one person's need to change modify amend correct alter fix chop to pieces EDIT improve another's copy’. The habit of correction is an ancient one, appearing long before the third‐century BC Alexandrians compared manuscript texts, or the sixteenth‐century scholar Erasmus reviewed the printed sheets of his books as they came off the press. Once upon a time correction (let's call it editing) had status and legitimacy, but in the past three centuries it has been de‐legitimized successively by the forces of romanticism, scientific empiricism, conflicting forms of critical theory, and the excesses of popular journalism. Susan Greenberg sets out to craft a general theory of editing that will respond to what she sees as its undeserved invisibility, which as a professional editor she feels keenly.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.035 |
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".