MétaCan
Menu
Back to cohort
Record W4384199742 · doi:10.32920/23676348.v1

Neither Here Nor There: Negotiating Hybridity in Second Generation Latinx Canadian Identities

2023· preprint· en· W4384199742 on OpenAlexaffabout
Claudia Klassen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicLatin American and Latino Studies
Canadian institutionsWilfrid Laurier UniversityToronto Metropolitan University
Fundersnot available
KeywordsHybridityIdentity (music)NegotiationIdentity negotiationSociologyRace (biology)First generationGeneration xGender studiesCultural identityThird generationPolitical scienceAnthropologySocial scienceDemographyArtAestheticsTelecommunicationsComputer scienceDemographic economics

Abstract

fetched live from OpenAlex

This study examines the diverse ways second generation Latinx Canadians form and negotiate hybrid identities. The forthcoming analysis relies on a literature review and other secondary research sources, namely blog entries and social media accounts such as Instagram. The question that guides this study is: How do second generation Latinx Canadians express and assert multiple cultural identifications (ie: being Canadian and being Latinx)? Other areas that are explored include comparisons to the first generation, pressures from society to identify a certain way, how their sense of belonging is impacted, and if age and race is a factor in influencing identity formation. The study finds that the employment of a hyphenated identity is the principal means by which the second generation in the Latinx Canadian community express their hybrid identity. The role of technology also proves to be significant as well as the ability to acquire and command the Spanish language. Key Words: second generation, Latinx, Canadian, identity, identity formation

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)0.002
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 score0.036
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0190.007
Scholarly communication0.0080.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.081
GPT teacher head0.323
Teacher spread0.242 · 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
Published2023
Admission routes2
Has abstractyes

Explore more

Same topicLatin American and Latino StudiesFrench-language works237,207