Diverging the Popular, Gender and Trauma AKA The Jessica Jones Anthology
Bibliographic record
Abstract
Jessica Jones made her first Marvel Comics appearance in Alias #1, November 2001, and went on to star in three ongoing series. In 2015 the Netflix adaptation Jessica Jones premiered to positive reviews. Following the scarred and superpowered titular character as she struggled to run a private detective agency and face her past, the show ran for three seasons and received a Hugo Award, a Peabody Award, and a Creative Arts Emmy. Diverging the Popular, Gender and Trauma AKA The Jessica Jones Anthology brings together a diverse group of scholars to explore the evolving depiction of the superheroine as embodied in both Jessica Jones and in the series. Contributors draw on trauma-informed study, lived experience, feminist approaches, cultural studies, and more to present multifaceted analyses. Specifically addressing survivorship, trauma, masculinities, and militarization, this book makes space for conversations that recognize the diverse, multi-layered narratives and complex, sometimes contradictory depictions presented by the show. Taking Jessica Jones as part of an evolving depiction of the superheroine, this anthology focuses not only on the content of the television series but female superheroes more broadly. It recognizes and critically discusses gendered and racialized roles and spaces, the changing expectations of fans, and the places in which media industries and fans interact. Connecting Jessica Jones to the wider Marvel Cinematic Universe, this is a thoughtful and thorough study of a ground-breaking character and boundary-pushing show.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".