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Youth Learning in Afterschool Programs

2015· book-chapter· en· W2475854713 on OpenAlexaffabout
Al Lauzon, Sarah Christie, Heather Cross, Bushra Khan, Bakhtawar Khan

Bibliographic record

VenueAdvances in educational technologies and instructional design book series · 2015
Typebook-chapter
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPositive Youth DevelopmentSituatedPolitical sciencePedagogyLifelong learningPsychologyPublic relationsDevelopmental psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This chapter reports on the learning outcomes of an after-school program (ASP) known as Fusion Youth and Technology Centre (Fusion) situated in Ingersoll, Ontario. The chapter begins by making the case that ASPs are part of the lifelong learning infrastructure and they should be given more consideration by researchers and policy-makers. This is followed by examining the changes in education and its implications for youth followed by a discussion of ASPs and positive youth development. A description of Fusion is presented and then the findings of four studies conducted at Fusion are reviewed. The discussion focuses on learning outcomes and reports back in terms of external and internal assets necessary for positive youth development to occur and their relationship to technical skill development. A conclusion is then drawn that ASPs with a focus on technology programs can have significant learning outcomes in terms of capacities and technical skills developed. Furthermore, it is argued that the benefts are often derived by rural youth who are not successful educationally, come from lower socio-economic homes, and are the youth who are most likely to be at-risk.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.074
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.040
GPT teacher head0.302
Teacher spread0.262 · 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 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
Published2015
Admission routes2
Has abstractyes

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