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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".