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Record W4236166542 · doi:10.32920/ryerson.14648847.v1

Ethnic communities, identity and belonging: an autoethnography

2021· preprint· en· W4236166542 on OpenAlexaff
Evanilde Bekkout

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicSocial Representations and Identity
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMulticulturalismIdentity (music)Ethnic groupAutoethnographySociologyEthnographyPortugueseGender studiesCultural identityArgument (complex analysis)Cultural group selectionAnthropologySocial scienceAestheticsPedagogy

Abstract

fetched live from OpenAlex

This paper presents an autoethnographic analysis of identity and belonging. I describe some of my own experiences as a member of the Brazilian and Portuguese communities in order to propose that multicultural individuals need to navigate among the many identities they relate to, which make them live at the margins of cultural groups rather than limiting them to one exclusive culture. I examine the period I lived in the Brazilian community and why I do not fit there; and why I fit in the Portuguese community although I do not feel that I fully belong there. Then, I argue that a multicultural individual needs to live at the margins of cultural communities in order to move among different communities; and conclude that more work on multicultural identity is needed to understand how multicultural individuals handle belonging without identifying themselves with specific groups. The sequence of personal experiences presents a progressive development of identity and belonging culminating with an argument that this project can be enlarged into a qualitative study of identity and belonging in ethnic groups to examine multicultural identity in ethnographic studies.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.147
GPT teacher head0.446
Teacher spread0.299 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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