Bibliographic record
Abstract
This paper explores the ways that critics writing in the early nineteenth century developed arguments in favor of what we think of today as the humanities in the face of utilitarian pressures that dismissed the arts as self-indulgent pursuits incapable of addressing real-world problems. Its focus reflects the extent to which the financial crisis in our own day has manifested itself in a jarring shift in research priorities towards applied knowledge: a retrenchment which has foregrounded all over again the question of how to make the case for the value of the humanities. These problems, however, also constitute an important opportunity: a chance to re-imagine our answers to questions about the nature and role of the humanities, their potential benefits to contemporary life, and how we might channel these benefits back into the larger society. The good news is that in many ways, this self-reflexive challenge is precisely what the humanities have always done best: highlight the nature and the force of the narratives that have helped to define how we understand our society—its various pasts and its possible futures—and to suggest the larger contexts within which these issues must ultimately be situated. History repeats itself, but never in quite the same way: knowing more about past debates will provide a crucial basis for moving forward as we position themselves to respond to new social, economic, technological, and cultural challenges during an age of radical change.
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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 | 0.040 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.017 | 0.186 |
| Scholarly communication | 0.026 | 0.039 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".