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Record W2971521903 · doi:10.31542/j.cb.1838

Educational Responses to Socioeconomic Inequality

2019· article· en· W2971521903 on OpenAlex
Laura Bures

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCrossing Borders Student Reflections on Global Social Issues · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsMacEwan University
Fundersnot available
KeywordsSocioeconomic statusInequalityGovernment (linguistics)Social inequalityPoliticsSocial mobilityEconomic growthPolitical sciencePsychologyMedicineEconomicsEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

Socioeconomic inequality continues to be a major concern both internationally and within Canada. Educational outcomes for children are one of the key areas affected by this reality. Schools are considered institutions responsible for promoting the social mobility of children. However, due to increasing social, political, and economic disparities among families, schools have redesigned themselves to ensure this idea persists. This paper examines how parental inconsistencies, lack of supportive home environments, and financial burdens associated with low socioeconomic status families have a negative influence on children’s educational outcomes. It investigates why schools have become concerned with implementing programs to help alleviate the effects of socioeconomic inequalities on children and their families. A discussion of the various strategies schools have put in place to integrate struggling children, families, and communities is included. Issues arise in regard to how these programs will be funded, who is responsible for these children within schools, and recommendations going forward. School boards need to be allocated more funding and support from macro level institutions such as the government and health boards if they hope to find a solution.

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.

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 categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
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
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.046
GPT teacher head0.496
Teacher spread0.450 · 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