Critical Latinx Indigeneities: Love Letters to Chicanx and Latinx Studies
Bibliographic record
Abstract
Migration and global displacement of populations in Latin America and the Caribbean have increased US Latinxs intergroup diversity, propelling fields like Chicanx Studies and Latinx Studies (CSLS) to question the taken-for-granted homogeneity of Latinidad and Chicanismo. Inter-group oppression within these imagined collectives has also shed light on overlapping colonial systems, specifically launching critiques of Latinidad that include the historical centering of cisheteronormativity within Chicanismo, AfroLatinxs, AsianLatinxs, and Indigenous erasure, as well as anti-Blackness and anti-Indio sentiments. Such critiques are pushing CSLS into a moment of reckoning. This article provides a “loving critique” of CSLS with an emphasis in education where innovative analytics like Critical Latinx Indigeneities (CLI) and Culturally Sustaining Pedagogy (CSP) are pressing these fields into unpacking and reorienting in ways that challenge the very narratives and discourses that in previous generations were empowering. A “loving critique” of CSLS is taken up by asking, what does CSLS need to re/think given the current diversity of “Latinx” communities? What can CSLS learn from CLI and CSP without appropriating or romanticizing them? How can CSLS not just survive but thrive and endure by engaging and learning from the opportunity that change brings?
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.039 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".