Weapons of male destruction : traumatized masculinity and the instrumentation of violence in Faulkner's Sanctuary and Light in August
Bibliographic record
Abstract
This thesis focuses on the disturbed and disturbing male characters in Faulkner's Sanctuary and Light in August, particularly Popeye and Joe Christmas.Published just one year apart,the two novels both explore constructions of white masculinity and the trauma that not only leads to but also follows in the wake of overtly ritualized performances of maleness.This thesis engages in an in-depth analysis of all of the male characters in both novels, who share a figurative, sometimes even literal, impotence that can be paradoxically attributed to theories of neurosis, anxiety and trauma.Using Freud as a springboard, this thesis proposes that the instrumentation of violence in these novels is the traurrtatized male's response to the culture which has caused the trauma.Ultimately, this thesis provides a significant context for reading Joe Christmas and Popeye's violent traits as traumatized responses to the definitions and impositions of masculinity produced by a culturethat valorizes assertions of aggressive power in men.I am most grateful to Dr. Emory Estes and Dr. Lauren Porter at the University of Texas at Arlington for introducing me to Faulkner during my undergraduate years.I will never forget their enthusiasm, passion and wisdom.I only hope someday I might positively influence a budding scholar as they have undeniably inspired me.In addition, a special thank you must be made to my colleague and friend, Vivi Dabee, for helping me shape my complicated ideas into comprehensive sentences -her motivation in my darkest hours provided me with all the light I needed.I would also like to express my extreme and sincere appreciation for Rose Fiorillo, Marianne Hamish and Mabelle Magsino, who helped me navigate the academic jungle and kept me pointed in the right direction when I was lost and frightened.Without their assistance and knowledge, I doubt I would have found my way.Furthermore, my gratitude to my family knows no bounds.Without the intellectual, emotional and financial support of my loving parents, Terrence A. and Zenaida "Suffry" Graham, my success would not have been possible.They are two of my biggest heroes and I would like to thank them for being shining examples and, of course, for allowing me to be their favorite
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".