A Field of Magpies: Disciplinary Emergence as Modus Vivendi in English Studies
Bibliographic record
Abstract
Our summons to draw “Lessons from the Past: The Emergence of University English” evokes, inescapably, some prominent institutional dimensions. These deserve such careful attention at present that I must acknowledge them in starting and, although the following discussion will chiefly trace out other dimensions of the topic, must return to them in closing as well. To the extent that “University English” involves matters such as organizational authority and clout, departmental viability and adaptation, the task of an institutional analysis will be to refine and extend inquiries that peaked a quarter-century ago when a handful of talented literary scholar-critics, sniffing if not biting the hand that fed them, applied their talents to assessing the institutional structure that sustains professional study of English.1 Especially amid the company I’m to keep in these pages, I have just enough awareness of the variance obtaining among institutional arrangements across the years and around the globe to sense how shakily I grasp even the conditions outside the USA that are best known to me, those in the UK and Canada. An aspirant to general overview who knows as little as I do about a world of anglophone institutional histories – one that embraces, most saliently, the several distinct histories comprised by Australasia – had better study silence first. -1st paragraph of text
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.053 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".