Therapeutic Reading: Self-Reflection and Social Awareness in Contemporary American Literature
Bibliographic record
Abstract
This dissertation examines the social function of literature for Oprah's Book Club (OBC) in comparison to how Dave Eggers's imagined audience approaches his fiction and nonfiction.By comparing these two groups of ideal readers, this project explores how certain reading communities understand reading and authorship to relate to therapeutic culture, self-transformation, social awareness, and, in some cases, social engagement.Understanding "therapy" broadly to mean the effort to transform oneself in response to emotional or physical distress, this project builds on scholarship which argues therapy sits at the heart of many contemporary approaches to literature.When reading therapeutically, literature is a tool used to understand the self in relation to others and in response to current events.Reading selections of work by Jonathan Franzen and Dave Eggers as well as engaging with episodes of The Oprah Winfrey Show and OBC discussions, this project explores how OBC and Eggers encourage their ideal audiences to improve themselves therapeutically by reading in similar but distinct ways.OBC and Eggers similarly direct their ideal audiences to transform themselves while reading by identifying with a work's author or characters.Likewise, they similarly believe literature holds the potential to inspire social awareness and a sense of social responsibility for their respective literary communities.For OBC, however, readers benefit from books by connecting literary works to their authors's biographies to identify with however an author seems to improve him-or herself by writing.In contrast,
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 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".