Curricular trauma? Producing converts?: A conversation between Caroline Levine and Len Gutkin
Bibliographic record
Abstract
The decline of the humanities has never been more topical. Proponents of the humanities of all political stripes have mounted defences, some more convincing than others, but for the most part, numbers continue to go down. In an article that appeared in Liberties and more widely in The Chronicle of Higher Education (12 July 2024), Len Gutkin broaches this issue. ‘Are the causes of the crisis external to the humanities’, he asks, ‘or do they reflect something gone awry in humanistic study itself?’ For Gutkin, we scholars should take some responsibility for the decline: the rise of subfields in English, for instance, has led to a dramatic contraction of the field in general. Critical race studies, for instance, may lead us to read Shakespeare too narrowly, and so on. Gutkin takes issue with the work of formalist scholar Caroline Levine. In this conversation, Levine responds to Gutkin, drawing on her decades of work both in the humanities classroom and in activism, and Gutkin to Levine. This dialogue informs our understanding of not only the classroom but also formalism and its applications.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.028 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".