Bibliographic record
Abstract
Women-led serials have been getting a lot of attention lately for bringing “the female gaze” to the small screen. Jill Soloway—the television auteur behind Transparent (Amazon, 2014–) and the recent adaptation of Kraus's novel, I Love Dick (Amazon, 2017–)—even taught a class on “The Female Gaze” at the Toronto International Film Festival in 2016, defining it as “an intersectional gaze” and “a SOCIOPOLITICAL justice-demanding way of art making.” But the female gaze is actually a very vexed concept. Since it was first invoked via exclusion in Laura Mulvey's foundational “Visual Pleasure and Narrative Cinema” in 1975, it has been haphazardly defined more often by what it is not than by what it is. Three current series—I Love Dick, GLOW, and Insecure—all explore how women empower themselves through experiences of abjection: states of vexation and alienation that disrupt their expectations of or participation in social life. All three shows demand respect for their characters by figuring defeat, failure, and desperation as stages women must pass through to challenge patriarchal cultures. While all three shows feature diverse casts and strong female leads, I Love Dick and GLOW introduce characters of color only in supporting roles that contest but never destabilize the white protagonists' racial solipsism. This strategic but facile gesture reveals how far these shows have to go to confront the entangled injustices of social inequality. To incorporate the experiences and insights of women of color meaningfully, their creators would have to abandon the narrative commitments and familiar pleasures of white feminist television, which still needs to decenter whiteness both narratively and figuratively. Insecure's trenchant comedy thus provides a model for future feminist television. Its self-critical but antiracist humor challenges white feminism's and television's historic neglect of black women.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.774 | 0.665 |
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".