According to Whom am I Happy? Identity Formation and Transfeminist Care Ethics in Imogen Binnie’s Nevada
Bibliographic record
Abstract
In this essay, I will provide an analysis of Imogine Binnie’s 2013 novel Nevada. I argue that Nevada is a counter-narrative to both traditional depictions of the road narrative as well as mainstream understandings of transition narratives. Drawing on Lauren Berlant’s concept of “Cruel Optimism” and Sarah Ahmed’s critique of cultural understandings of happiness in her essay “Unhappy Queers,” I will show how Binnie complicates normative understandings of trans identity which rely on trans people assimilating into cisgender heterosexual society. I then read the brief connection between Nevada’s two main characters, Maria and James, through Amy Marvin’s “Transfeminist Care Ethics” to show how Binnie rejects the impulse towards individualistic self-realization and instead posits the complicated and sometimes painful connections between transgender subjects as the real site of James and Maria’s identity formation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".