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The Role of Afterschool and Community Science Programs in the Lives of Urban Youth

2005· article· en· W2018407622 on OpenAlexaffabout
Jrène Rahm, Marie‐Paule Martel‐Reny, John C. Moore

Bibliographic record

VenueSchool Science and Mathematics · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsConcordia UniversityUniversité de Montréal
Fundersnot available
KeywordsPositive Youth DevelopmentScience educationAction (physics)EthnographyScience learningSociologyMathematics educationYouth engagementPedagogyPsychologyPublic relationsPolitical scienceDevelopmental psychology

Abstract

fetched live from OpenAlex

Afterschool and community science programs have become widely recognized as important sanctuaries for science learning for low‐income urban youth and as offering them with “missing opportunities.” Yet, more needs to be known about how youth, themselves, perceive such opportunities. What motivates youth to seek out such opportunities in the nonschool hours? How do youth describe the doing and talking of science in such programs? Given such descriptions, how do youth perceive the role of these programs in their lives? This paper relies on stories from three youth drawn from a multisited ethnographic study, one site being an afterschool girls‐only science program at the elementary level in Canada and the other an Upward Bound Math and Science program in the USA. The paper concludes with a discussion about the ways these programs offered youth a meaningful way to relate to science in concordance with their own lived experiences, resulting in “I will” and “I can” attitudes and a sense of hope for the future within which science becomes a tool for action.

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)0.003
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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0130.007
Scholarly communication0.0060.002
Open science0.0010.007
Research integrity0.0010.002
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 head0.038
GPT teacher head0.296
Teacher spread0.258 · 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

Citations51
Published2005
Admission routes2
Has abstractyes

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