Speaking out: Class, race, and gender in the writings of Ruth McEnery Stuart, Edith Summers Kelley, and Harriette Simpson Arnow
Bibliographic record
Abstract
This study analyzes representations in American fiction of social issues during periods of national turmoil. Ruth McEnery Stuart, Edith Summers Kelley, and Harriette Simpson Arnow draw characters marginalized by class, gender, race, and ethnicity who negotiate survival amid crisis and adverse socioeconomic conditions. Giving voice to people who could not speak for themselves, the texts perform the work of cultural resistance to a discourse that privileges capitalism, patriarchy, and white supremacy while oppressing the poor, women, and non-whites. Following an introductory chapter, I present the authors and their works chronologically, beginning each chapter with a brief overview of the historical moment to which that author responds. The literary analyses that follow examine the details of daily life as represented in the texts in order to study how these works speak to and are produced by their cultural moments. Chapter Two summarizes ever-evolving interpretations of Reconstruction and analyzes writings by Stuart, a southern writer from the late nineteenth century. Subversively using humor, dialect, and a broad range of characters, she cuts across lines of class, race, gender, and ethnicity to challenge social barriers. The third chapter reviews the Progressive Era, a period known for social reforms and technological advances. Analysis of Kelley's 1923 Weeds, set in a remote Appalachian community during World War I, exposes the impact of economic, biological, and social forces on women's lives. Chapter Four begins with an overview of the quarter century after World War I, including the Depression and the Second World War. Arnow's 1954 The Dollmaker is analyzed for the effects of dislocation and economic insufficiency on the life and spirit of a woman who migrates with her family from Appalachia to the industrial wartime city of Detroit. The literature in this study is specifically American and is strongly connected to place. Though it enjoyed initial critical acclaim, it is currently marginalized outside the canonical mainstream. Because they speak for members of our society whose hardscrabble lives, while difficult to confront, form an integral part of our national mosaic, these works merit reconsideration for American Studies and examinations of race, working-class issues, and women's concerns.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".