Monsters and Madwomen? Neurosis, Ambition and Mothering in Women Lawyers in Film
Bibliographic record
Abstract
In this article we engage with the writings of feminist scholars Sandra Gilbert and Susan Gubar, whose landmark work The Madwoman in the Attic critiques the image of the female madwoman or monster. We use Gilbert and Gubar’s thesis of the female monster as the primary analytical framework for excavating three variants of female madness as depicted in three films: madness as neurosis using Laws of Attraction; monstrosity as ambition using Michael Clayton; and madness/monstrosity as failed motherhood using I Am Sam. Our goals for this article are to explore the ways in which popular films featuring female lawyers channel the “madwoman/monster” metaphor; trace those characters in terms of neuroses, ambition, and motherhood; and argue for the possibility of reconfiguring the notion of “madwoman” as a valid and meaningful mode of female subjectivity that expands the field of possibilities for women lawyers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".