Pluralizing the Sublime in the Post-Romantic American Memoir: From the Visual to the Haptic
Bibliographic record
Abstract
The Romantic sublime, which has influentially shaped the Western relationship with nature, is no longer viable in the Anthropocene because it views nature as separated from humans and does not fully represent the intricacy of human/non-human relationship (Caracciolo 2021). This paper assesses the limits and affordances of the notion of the “haptic sublime” (McNee 2016) for challenging the Eurocentrism, androcentrism, and ocularcentrism of the Romantic sublime in a way that would also lead to heightened self- and/or environmental awareness. To that end, it analyzes extracts from American ecobiographical memoirs that build on American Romanticism’s, and more specifically Henry D. Thoreau’s multisensorial approach to nature (Lombard 2019), which suitably integrates the subjective and embodied dimensions of the haptic sublime. Inspired by insights from affective ecocriticism and rhetorical (eco)narratology, these analyses show that the haptic sublime involves bodily and multisensorial contact with material environments which induce affects such as guilt, satisfaction, and joy, that move beyond the confines of the romantic experiences of awe and horror. Jon Krakauer’s Into Thin Air and Silvia Vásquez-Lavado's In the Shadow of the Mountain are considered so as to argue that recent mutations in the rhetoric of the sublime retrospectively unearths haptic dimensions to the experience of the sublime.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".