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Unpacking the Opportunity Costs of Summertime Programs in Canada: A Crucial Examination and Policy Implications on Education and Social Equity

2024· article· en· W4404282437 on OpenAlexaboutno aff
Obianuju Juliet Bushi

Bibliographic record

VenueInternational Journal for Infonomics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsUnpackingEquity (law)Social equalityBusinessEconomic growthEconomicsPolitical science

Abstract

fetched live from OpenAlex

This paper examines the opportunity costs associated with current Summertime programs (SP) available to school-aged children and youth in Canada, while focusing on social disparities and policy implications.The lack of a nationwide quality SP in Canada further contributes to social reproduction and widens the opportunity gaps for families and students.Privatized and institutionalized SP costs have continued to increase despite the challenging economic situation in Canada.Limited financial support for families has contributed to SP becoming a buffet of unaffordable programs only accessible to the elites and upper class.While current SP aims to provide educational enrichment and skill development, it may inadvertently overlook the broader needs of students and further reinforce systemic barriers.This analysis explores the effectiveness of these programs, funding implications, and policy recommendations to enhance the impact and equity of Summertime initiatives.This paper also calls for a policy change.A transformational re-imagination and restructuring of our current K-12 education and the role of government in education and society.

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)0.002
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.454

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0050.003
Scholarly communication0.0050.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0120.000

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 head0.147
GPT teacher head0.449
Teacher spread0.302 · 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

Citations2
Published2024
Admission routes1
Has abstractno

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