“Everything Being Tangled Up in Every Other Thing”: Class, Desire, and Shame in Michelle Tea's<i>The Passionate Mistakes and Intricate Corruption of One Girl in America</i>
Bibliographic record
Abstract
This article explores the relationship of shame to class and to desire in Michelle Tea's memoirThe Passionate Mistakes and Intricate Corruption of One Girl in America. Through applying a class analytic to the framework of shame recently advanced by feminist, queer, postcolonial, and affect theorists, I foreground shame as central to the experience of being poor and queer, and examine shame as not only negative and positive, but as productive. I operationalize an “oppositional reading strategy” to insist on attention to the materiality of embodied desire and labor, in particular queer desire and sex work, that is made available in poor and working‐class women's life‐writing. Tea's memoir demonstrates how writing about an ambivalent relation to shame is an act of resistance, an opportunity to transform private, individual experiences into public, and therefore collective, articulations.
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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 | 0.000 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".