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Record W4393409846 · doi:10.1080/15348431.2024.2332756

Critical Latinx Indigeneities: Love Letters to Chicanx and Latinx Studies

2024· article· en· W4393409846 on OpenAlexaff
David W. Barillas Chón, Judith Landeros, Luís Urrieta

Bibliographic record

VenueJournal of Latinos and Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsWestern University
Fundersnot available
KeywordsOppressionGender studiesSociologyIndigenousDiversity (politics)NarrativePoliticsPolitical scienceAnthropologyLawArt

Abstract

fetched live from OpenAlex

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?

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.450
Teacher spread0.419 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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