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Record W7112380492

Searching for Belonging and Living with Saudade: Emotional Geographies of Brazilian LGBTQ+ Migration to Canada

2025· other· en· W7112380492 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsQueerFeelingGeopoliticsIdentity (music)OperationalizationIntersection (aeronautics)PrecarityTemporality
DOInot available

Abstract

fetched live from OpenAlex

Many LGBTQ+ Brazilians drastically remap their lives by emigrating to Canada, having often been denied place-making and the safe (re)production of their queer identities in their homelands. This research traces such northern geopolitical migration trajectories and seeks to understand them at the intersection of queer and emotional geographies. Particular attention is directed to interpreting the feeling of saudade, a cultural emotion specific to the Lusophone sphere. Drawing upon 14 semi-structured interviews, a focus group, and 29 photographs of objects that symbolize saudade, I operationalize the multi-scalar relations between the queer Brazilian body and the home, the nation, and transnational flows. The findings of this research outline those relationships, highlight spatially contingent and transforming othering processes, and explores the emotional processes that lead to a queer Brazilian diasporic identity of in-betweenness, physically in Canada, but oriented towards queer Brazil.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.079
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.007
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.004
GPT teacher head0.155
Teacher spread0.150 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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