“I Save Me”: Gender, Agency, and Power in Better Call Saul
Bibliographic record
Abstract
Historically, women on television have been portrayed in wife and mother roles, making them a foil to their husbands, but never the main focal point of the show. These characters stay on the sidelines, without being given truly original storylines where they are allowed to drive their own narratives. During the first season of Better Call Saul, Kim Wexler is a supporting character, without any storylines that aren’t linked to Jimmy McGill. Jimmy often treats Kim as a damsel in distress. He thinks it’s his job to save her, and usually from the chaos that he’s created. In this thesis paper, I explore how the male-dominated world of the Breaking Bad universe is transformed into a female-led narrative through Kim Wexler in Better Call Saul. In reviewing the show, gender studies, and the role of women on television, I argue that the Kim character must overcome gender constraints from contemporary capitalism, big law, marriage and family, the law itself, and ultimately her own partner to become the protagonist of the show. As she challenges each of these things, Kim ultimately gains control of the show’s narrative and Jimmy’s fate. As viewers speculate what the final season of Better Call Saul has in store for Kim, it’s clear that whatever happens to Jimmy is because of Kim. She is what has motivated most of Jimmy’s schemes, and her presence, or lack of presence, will decide what motivates Jimmy to fully commit to his Saul Goodman persona in Breaking Bad, connecting his agency to Kim’s choices, not the other way around.
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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 | 0.003 | 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 | 0.018 | 0.026 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".