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How Positive Youth Development Can Support Low-Income Roma Youth Living in the United States

2021· book-chapter· en· W3200312248 on OpenAlexaboutno aff
Marija Bingulac

Bibliographic record

VenueOxford University Press eBooks · 2021
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Systems and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPositive Youth DevelopmentPolitical scienceYouth studiesMainstreamInjusticeEthnic groupPovertyCriminologyEconomic growthGender studiesSociology

Abstract

fetched live from OpenAlex

Deprivation and discrimination, including the destruction of housing settlements, forced evictions, and persistent violence, led a portion of Europe’s 12 million Roma to seek refuge in the United States and Canada. Approximately 1 million Roma live in the United States, and 80,000 Roma currently live in Canada. Profound experiences of injustice in their home countries have led Roma in the United States to keep their lives hidden from mainstream society. The Roma as a race/ethnicity is not accounted for in any American surveys, and research on their well-being in the United States is scarce. This chapter fills knowledge gaps by presenting a one-of-a-kind comprehensive literature review synthesizing empirical evidence on the lives of Roma people and their youth in the United States by applying the positive youth development (PYD) framework that focuses on promoting positive asset-building for youth and seeing youth as vital resources in development strategies. In doing so, the chapter advances beyond the more usual narrative that has focused on the problems of Roma youth to examine the mechanisms that can enable them to flourish in the United States. Romani youth is a case study example of youth of color in general; this chapter adds to the body of knowledge that examines how PYD development matters for positive developmental outcomes of a minority group that has experienced socioeconomic disparities strictly because of the stigma of their identity.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.947
Threshold uncertainty score0.598

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 science0.0010.000
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 head0.032
GPT teacher head0.182
Teacher spread0.150 · 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 designNot applicable
Domainnot available
GenreOther

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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