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Record W4405462340 · doi:10.1080/24694452.2024.2431329

Unsettling Black, Indigenous and Queer Latinx Senses of Place and Radically Remapping Latinx Geographies of Belonging in the City

2024· article· en· W4405462340 on OpenAlexaboutno aff
Madelaine C. Cahuas

Bibliographic record

VenueAnnals of the American Association of Geographers · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicLatin American and Latino Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyQueerIndigenousGender studiesSolidarityHegemonyPraxisIntersectionalityReciprocity (cultural anthropology)AestheticsPoliticsAnthropologyPolitical scienceArtLaw

Abstract

fetched live from OpenAlex

This article examines the radical possibilities of Latinx senses of place and geographies. I do this by deeply engaging with the film series, Will You Listen? by Kichwa artist Samay Arcentales Cajas, which explores Latinx people’s experiences and relationships to place in Tkaronto (Toronto, Canada). Weaving an intersectional, interdisciplinary, and geographic framework and methodology, I demonstrate how Black/Afro-Latina, Indigenous, and racialized queer and nonbinary Latinx people featured voice distinct unsettling Latinx senses of place. I offer unsettling Latinx senses of place as a concept that speaks to how Latinx people understand, navigate, and make place through an embodied anticolonial feminist praxis, which challenges how intersecting relations of conquest and power shape their everyday lives and the spaces they inhabit. Unsettling Latinx senses of place engender refusals of normative claims to space and belonging in relation to the city, the nation-state, and hegemonic Latinidad, while also illustrating new ways of belonging grounded in relationships of care, reciprocity, and solidarity. Overall, I aim to enrich understandings of Latinx senses of place and contribute to the growing field of Latinx geographies in a way that works toward more just and liberatory geographies within and beyond the discipline.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
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 head0.018
GPT teacher head0.307
Teacher spread0.289 · 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 designObservational
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

Explore more

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