<i>Textuality and Knowledge: Essays</i> . By <scp>Peter L. Shillingsburg</scp> <i>Textuality and Knowledge: Essays.</i> By ShillingsburgPeter L..University Park, Pennsylvania: The Pennsylvania State University Press. 2017. xii + 222 pp. $115 (hardback); $44.95 (paperback). <scp>isbn</scp> 978 0 271 08107 6 (hardback); 978 0 271 07850 2 (paperback).
Bibliographic record
Abstract
Peter Shillingsburg’s collection of recent essays and talks records the thinking of one of our strongest editorial theorists as the study of the book bent— or did not bend—to the winds of change during the first decade of the millennium. As such it asserts the principles of a strict empiricist who is at the same time attempting to defend his position with respect to the different claims of historical editing, social editing, and digitization. His preface functions as an introduction to those principles: ‘Sound evidence undergirds knowledge; unsound evidence cannot lead to or support knowledge—except by accident …. In literary studies all evidence is textual. It depends on documents, document preservation, and textual replication. Interpretative strategies are for understanding the evidence’ (p.vi)....
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.247 | 0.172 |
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".