Expressed silence: a study of the metaphorics of word in selected nineteenth-century American texts
Bibliographic record
Abstract
Expressed Silence: A Study of the Metaphorics of Word in Selected Nineteenth-Century American Texts This dissertation explores the patterned use of certain “metaphors of word”——images of reading, writing, listening, and speaking——in four American texts: Emerson’s Nature, Thoreau’s Walden, Hawthorne’s Scarlet Letter, and Melville’s Moby Dick. Assumed in my discussion is the modern view of metaphor as a cognitive device used, not for mere stylistic ornament, but for creating a certain mental perspective. Based on the perspectival view and on the experiential—gestalt account of metaphor, the structures of these metaphors of word are examined in order to discern the systematic nature of their argument and to determine the cultural and historical reasons why language imagery, and not some other type of imagery, was chosen to represent this argument. After surveying the cultural influences of democracy, mercantilism, Romanticism, and Calvinism, I characterize the metaphoric systems of each text and then move on to a closer study of the role of silence within these systems. From this analysis, I conclude that these nineteenth— century texts reflect a shift away from the book toward the voice as a predominant symbol, and away from writing toward speaking as a privileged metaphor. Language imagery works to represent ways of knowing, so that linguistic and epistemic concerns become inextricably intertwined. The process of using language operates as a metaphor for the process of gaining knowledge. In this metaphorics of word, silence emerges as a particularly striking metaphor in the way that it expresses the coalescence of being and knowing, the realization that we know what we know. In this scheme, metaphors of word structure ways of understanding, and the expressed silence metaphor highlights the way interior speech can function in the discernment of knowledge. Ultimately, I contend that the perspective provided by this nineteenth—century metaphorics of word forecasts the modern view of rhetoric as epistemic. By employing linguistic action as a figure for representing epistemic action, a metaphorics of word promotes an understanding of rhetoric’s primary purpose as the interrogation of truth.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".