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Record W4307983390 · doi:10.32920/21437226

Latinx Experiences in the Ontario Education System: Race, Invisibility, and Capital

2022· preprint· en· W4307983390 on OpenAlexfundaboutno aff
Henry Parada, Veronica Escobar Olivo, Fabiola Bravo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDiverse Education Studies and Reforms
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInvisibilityDisadvantageCritical race theorySocial capitalFocus groupCultural capitalSociologyGender studiesQualitative researchRace (biology)PedagogySocial psychologyPsychologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

<p>This article explores Latinx youth’s experience in the Ontario education system and the level of support they encounter throughout their education. Based on phenomenological qualitative interviews, this study found that Latinx youth in Ontario lacked sufficient support networks throughout their childhood and adolescence. Using Bourdieu’s theory of capital and education, as well as Latino Critical Race Theory (LatCrit), this research explores how a lack of access to relevant capital positions Latinx youth at a disadvantage in their educational attainment and how their unique experiences are unrecognized. This article is based on a total of 52 participants (27 one-on-one interviews and 23 participants in two focus groups). The findings indicate that youth perceive parents to have limited social networks, skills, and knowledge necessary to assist their children in the Ontario education system. School teachers, guidance counsellors, and principals who are positioned to provide supplemental support for marginalized youth often failed to provide any significant guidance. Youth were faced with finding alternative modes of support or depending entirely on themselves. Given the lack of discourse surrounding the unique experience of the Latinx minority, the experiences of Latinx youth remain mostly unseen.</p>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.333
Teacher spread0.296 · 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 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
Published2022
Admission routes2
Has abstractyes

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