Bibliographic record
Abstract
Abstract From climate catastrophe to pandemics and economic crises, the problems facing humanity today are impossibly complicated and planetary in scale. Critical Modesty in Contemporary Fiction makes the surprising but compelling claim that it is precisely by culitvating a modest temperament that contemporary fiction can play an central role in conbating the despair that many of us feel in the face of such enormous and intractable problems. This new temperament of critical modesty locates the fight for freedom and human dignity within the limited and compromised conditions in which we find ourselves. Through readings of Ian McEwan, Zadie Smith, J. M. Coetzee, and David Mitchell, Critical Modesty in Contemporary Fiction shows us how contemporary works of literature model modesty as a critical temperament. Exploring modest forms of entangled human agency that represent an alternative to the novel of the large scale that have been most closely associated with the Anthropocene, Dancer builds a case that the novel has the potential to play a more important socio-cultural role than it has done. In doing so, the book offers an engaging response to the debate over post-critical and surface readings, bringing novels themselves into the conversation and arguing for a fictional mode that is both critical and modest, reminding us how much we are already engaged with the world, implicated and compromised, before we start developing theories, writing stories, or acting within it.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".