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Record W4235113831 · doi:10.32920/ryerson.14657271

Latino Youth and Machismo: Working Towards a More Complex Understanding of Marginalized Masculinities

2021· preprint· en· W4235113831 on OpenAlexaffabout
Ramon Meza Opazo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMasculinityMainstreamOpposition (politics)Gender studiesSociologyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Academic and mainstream discourses have discussed Latino youth machismo in overwhelmingly negative terms, defining it as misogynistic, reckless, and violent. Even the sociological studies that have conceptualized machismo as a byproduct of social marginalization posit it as inherently destructive. Some emerging American literature has sought to consider the positive aspects of Latino masculinity through explorations of familism and caballerismo, but these have been set in opposition to, as opposed to a part of, machismo. This study aims to address post-structuralist calls for a more positive exploration of machismo by considering the ways in which Latino youth in Toronto conceive of their masculinities in relation to familism and social integration. Data emerging from focus group discussions suggest that these youth rely on machismo to assist in their integration into the Canadian labour market, their survival in the streets of their communities, and that there is a gendered basis to their adherence to familism.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.107
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0090.030
Scholarly communication0.0100.007
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.309
GPT teacher head0.357
Teacher spread0.048 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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