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Record W2980764833 · doi:10.1353/ces.2019.0007

Centering the White Gaze: Identity Construction among Second-Generation Jamaicans and Portuguese

2019· article· en· W2980764833 on OpenAlexvenueaboutno aff
Esra Arı

Bibliographic record

VenueCanadian ethnic studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCaribbean history, culture, and politics
Canadian institutionsnot available
Fundersnot available
KeywordsGender studiesPortugueseIdentity (music)RacializationWhite (mutation)Cultural identitySociologyRace (biology)AestheticsArtLinguisticsSocial science

Abstract

fetched live from OpenAlex

This article examines identity construction among second-generation Jamaicans and Portuguese in Toronto. The research question guiding this study is as follows: in a "multicultural" Canadian setting, are there differences between the ways second-generation diasporic Jamaicans and Portuguese define themselves, both ethnically and racially, and how others see them? To address this line of inquiry, I conducted 43 in-depth interviews with second-generation Jamaicans and Portuguese in the Greater Toronto Area (GTA). This study also seeks to lay bare the impact of racialization on identity construction. Since both the Jamaican and Portuguese participants of this study are mostly working-class immigrants for whom class is defined through a neo-Marxist lens, it is easier to discern the impact of race on identity construction when the role of class is held constant. Furthermore, comparison of the two groups is utilized to delineate distinct degrees of racialization within the two groups: one is a "visible" minority while the other is a non-visible minority. Based upon my interviews and my review of the literature, I argue that Portuguese are seen as dark-whites in Toronto due to their social class and their non-Anglo-Saxon culture. On the part of Jamaicans, society in general defines Jamaicans as black as a result of slavery, power relationships, and color symbolism.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.088
GPT teacher head0.340
Teacher spread0.253 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

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