Bibliographic record
Abstract
My dissertation focuses on some of the ways in which twentieth-century American writers have represented pain and suffering. I examine the narrative strategies used by Stephen Crane, William Faulkner, Richard Wright, Toni Morrison, and Claudia Rankine (among others) to depict the event of bodily distress as a biocultural phenomenon. I argue that these texts describe pain as a conscious experience that is entwined with language, memory, noxious stimuli, social codes of behaviour, racial identity, gender expression, and class. I anchor my analysis in scenes of suffering that situate the aches and torments of their characters within the broader narrative framework of American settler-colonial violence. My work examines two storytelling strategies commonly used in American texts: first, I analyze the trope of a naïve youth who stumbles upon spectacles of intense injury and suffering. Texts like Huck Finn and “Chickamauga” deploy this narrative design to showcase how jingoist war-mongering, militarized expansionism, gun violence, and racially-charged assaults all create a national climate of profound misery. This anguish manifests as physical injury but also, most crucially, in sensations of psychological distress caused by stress, confusion, and fear. In The Red Badge of Courage and Black Boy: American Hunger, witnessing pain blurs into participating in it, as the protagonists find themselves experiencing profound suffering as a response to their physical trauma and distressed outrage towards America’s cruel cultures. The second trope I examine is that of the weary writer who is tasked with the burden of documenting, diagnosing, and conveying the pain around them. Writers like Nathaniel West, James Agee, Philip Roth, and Claudia Rankine all craft scenes in which a storyteller reflects on the act of narrativizing pain in order to illuminate some of the ways in which pain is socially constructed. My dissertation analyzes the linguistic strategies used to bring pain to life on the page and convey how pain is inflicted and felt. As my chapters unfold, I will reveal how American texts render the phenomena of suffering as a linguistically-structured conscious response to violence, racism, class conflict, and a host of other social issues plaguing the nation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.032 | 0.025 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".