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Record W7017044672

Achievement inequity, students’ participation and curriculum offerings in mathematics and science Higher School Certificate (HSC) courses within a segregated school system in New South Wales, Australia

2020· dissertation· en· W7017044672 on OpenAlexaboutno aff

Bibliographic record

VenueThe Sydney eScholarship Repository (The University of Sydney) · 2020
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumCertificateGovernment (linguistics)Socioeconomic statusQuarter (Canadian coin)Academic achievementSchool systemHigher education
DOInot available

Abstract

fetched live from OpenAlex

The persistence of inequity in education in a developed country such as Australia has been widely studied, especially in terms of the relationship between achievement inequity and socioeconomic status (SES). Similarly, students’ participation and access in the so-called Key Learning Areas (KLA) in the Australian school curriculum has been focus of increasing concern, with data showing this is linked to students’ SES. However, there is minimal research exploring the influence of students’ socio-educational advantage (SEA) upon achievement inequity, students’ participation and access to KLAs, such as mathematics and science. Using a quantitative approach, this longitudinal cohort study explores achievement inequity along with trends in curriculum offerings and students’ participation in mathematics and science courses in a segregated school system in NSW, Australia. Using three different datasets: 1) the Higher School Certificate (HSC) Distinguished Achievers (DAs) list in 2011 and 2018, 2) data obtained from the My School website, 3) students’ enrolment and curriculum offerings in mathematics and science courses in 2011 and 2018. The study found statistically significant differences in achievement inequity between schools with high and low proportions of HSC DA students in both 2011 and 2018; along with evidence to show that school segregation and inequity have grown between those years. Unsurprisingly, the top HSC DA schools were principally private schools and government academically selective schools that concentrate a higher proportion of students from the highest quarter of socio-educational advantage (SEA). Similarly, students’ participation and curriculum offerings in mathematics and science courses show inequity as more socially advantaged schools had higher enrolments in those courses, especially in advanced mathematics and sciences.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.824

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0000.001
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 head0.045
GPT teacher head0.287
Teacher spread0.242 · 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 designObservational
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

Citations0
Published2020
Admission routes1
Has abstractyes

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