Intersectionality Meets Infrastructure: Recruitment Matrices and Identity Overflow in Just Research
Bibliographic record
Abstract
This paper traces intersectionality's theoretical-methodological "twists and turns" to reconsider its explanatory power in elucidating relations between selves and socialites and its application in research. Questions of how researchers take up the heuristic have become charged given intersectionality's uptake by democratic institutions as a marker of excellence alongside critiques of its appropriation by systems advancing it. Adopting a processual-relational framing, we argue that difference represents a site of possibility-affirming life's heterogeneity-and danger, exposing the unboundedness of monolithic identities upon which intersectional theorizing relies through misfitting/fracturing. This reveals intersectionality's potential as infrastructure. Using an "infrastructural inversion" that makes the hidden work of intersectionality-as-infrastructure perceptible, we demonstrate how an infrastructural critique uncovers the socio-material implications of classification systems underpinning intersectionality. We approach research matrices as "wild containers" illuminating nondominant differences, suggesting this enables a decolonized understanding of intersectionality as inter-/intra-sectional becomings moving beyond hierarchical categorizations imposed by white supremacist thought.
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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 | 0.033 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.078 |
| Scholarly communication | 0.018 | 0.036 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".