What's with All the Tapestries? Intersectionality and the Discursive Vacuum of Generative AI
Bibliographic record
Abstract
Abstract This article uses its three authors’ intersectional Jewish identities to critically investigate the cultural consequences of generative artificial intelligence services. It argues that generative AI's positivistic and denotative logics treat identity as an additive construct, which is functionally incommensurate with intersectional frameworks when users aim to generate content that pertains to multiple formulations of identity. By analyzing the outputs of the text-to-image generator Midjourney and the large language model ChatGPT for “shadow ontologies” of ethnicity, race, gender, and sexuality, it situates its argument within discourses on intersectionality driven by Black feminist scholars and queer theory critiques of social classification systems. Methodologically, this article employs two related experimental techniques rooted in diasporic Jewish epistemologies, which it calls kibbitzing and futzing. It concludes with a discussion of why technological approaches to rendering intersectional identities often fail, and it offers an alternative paradigm for thinking through the sociotechnical affordances of generative AI.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.066 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".