Fragments in the Flesh: an Autohistoria-Teoría of Disability and Decolonist Rhetorics
Bibliographic record
Abstract
This text is an Anzaldúan autohistoria-teoría, a genre that blends the autoethnographic with poetry, fiction, visuals, and theories rooted in narrative identity. Specifically, this dissertation is modeled after Anzaldúa’s own incomplete doctoral dissertation, Luz en el oscuro/Light in the Dark. In Anzaldúa’s final text, she continues her exploration of the new mestiza, but she tempers it with nuanced views on the particulars of identity, alongside deeply personal explorations of her understanding of herself as a chicana, an academic, and a person in an aging body. As with much of her work, she blends creative elements with her theory, including poetry, memoir, and drawings she made to illustrate her theoretical concepts (the autohistoria-teoría). In addition to this, I use Cherrie Moraga’s theory-in-the-flesh (a concept wherein theory is built on particular experience) to provide theoretical justification. I also borrow from Jaques Derrida, Edward Said, Gayarti Spivak, and Roland BarthesBy using Moraga’s and Anzaldúa’s ideas as a roadmap for my own writing, I place myself firmly within a feminist and queer framework, with a focus on decolonial and disability rhetorics. For this dissertation, I use autohistoria-teoría to explore historical traumas through a personal lens, as well as personal trauma through a historical lens. I propose four concepts in narrative identity in order to explore these ideas: los zorros (decolonial metis), pishtaco/Inkarri (decolonial hauntology), el tumi (disability metis), and el retablo (pedagogical concerns).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".