Bibliographic record
Abstract
Longstanding political, social, and academic debates surrounding women’s anger have followed a distinct pattern. On one hand, critics disparage women for writing and speaking in an angry voice, casting them as bitter, irrational, or they assign them the pejorative “angry feminist”. Women often respond to these critiques by defending their anger, and reframe this emotional response as a legitimate response to oppression. Despite the utility of this intervention, this debate has given rise to a binary structure where a woman’s anger is either a legitimate response to oppression, or an irrational emotional response. As a result, the alternative functions to women’s anger remain largely unexplored. Working against binary logic, this dissertation aims to reframe this debate, and answer the following questions: what are the alternative functions for women’s anger outside of the binary terms of this debate? How can literary representations of anger complicate this conversation? Drawing from affect theory, intersectional feminist theory, discourse analysis, feminist discourse analysis, philosophical discussions about emotion, feminist literary theory, and ongoing debates surrounding nostalgia, this dissertation explores the function of anger within contemporary Canadian and American women’s literature. Before undertaking literary analysis in subsequent chapters, this dissertation first develops a methodology of “imperfect alignment” to account for the tensions between affect theory and discourse analysis, the theories and methods that guide this research project. The second chapter explores the ways anger allows liminal subjects to come into view in Feinberg’s Stone Butch Blues and Morris’s A Dangerous Woman. Chapter three explores the ways anger can interrupt and complicate compassionate reader responses to gender based abuse in Sapphire’s Push and Mosionier’s In Search of April Raintree. Chapter four explores the ways anger and nostalgia allow subjugated groups to link anger to domestic violence in Joyce Carol Oates’s Foxfire and We Were the Mulvaneys. Finally, this dissertation concludes with a brief analysis of feminist critiques of reason, and locates the findings of this project in relation to this scholarship. Ultimately, this research project nuances debates surrounding anger, and poses alternative readings of this emotional response.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.032 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".