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Record W4416047881 · doi:10.35542/osf.io/dq7zt_v1

How affordable are independent school fees in Australia?

2025· preprint· W4416047881 on OpenAlexaboutno aff
Olivia Johnston, Laura B. Perry, M Sinclair, Brad Gobby

Bibliographic record

Venuenot available
Typepreprint
Language
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)TaxpayerProductivityGovernment (linguistics)Private sectorPublic sectorSchool choiceValue (mathematics)

Abstract

fetched live from OpenAlex

The independent private school sector in Australia is growing dramatically. While the sector on average enrols a larger share of socially advantaged students compared to the Catholic or public school sector, the extent to which independent school fees are financially accessible is not well known. Examining this question is important as the sector receives substantial taxpayer subsidies. We examined the fees charged by independent secondary schools in Australia’s five largest cities, collectively home to almost 65% of Australians. On average across all five cities, independent secondary schools charge approximately $15k per year, with 25% of schools charging less than $5k per year and another quarter charging more than $20k per year. Fees are highest in Melbourne and Sydney, where one-third of schools charge over $20k per year, and 10% charge more than $40k per year. Average fees varied across the five cities and are not easily explained by public policy or economic contexts. While a substantial number of independent secondary schools can be considered affordable, most are not. We query the value to Australian society of having so many high-fee independent schools and argue that Australia’s aim of increased productivity would be better met by diverting government funds spent on high-fee schools to students and schools with greater need.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient 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 score0.484
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.062
GPT teacher head0.348
Teacher spread0.286 · 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; both teacher heads agree on what is shown here.

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
Published2025
Admission routes1
Has abstractyes

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