Unsettling Black, Indigenous and Queer Latinx Senses of Place and Radically Remapping Latinx Geographies of Belonging in the City
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".