Policy structures in Australia, the UK and Canada
Bibliographic record
Abstract
One way to analyse early childhood education and care (ECEC) policy is to measure and compare total government expenditure on ECEC as a proportion of gross domestic product (GDP). While most countries across the Western world have increased investment in ECEC services as a percentage of GDP over the past decade (OECD, 2015, PF3.1), the ways in which spending is structured has changed. In addition to spending as a proportion of GDP, comparisons of ECEC policy also include: expenditure per child; access to and participation in formal, centred-based services; the affordability of centre-based ECEC as a proportion of average or median family income; and the quality of different service models. Affordability of services often reflects the role and level of responsibility the public sector has for funding and regulating services. Ultimately, affordability across different social groups – including income levels – provides an indication of the division of responsibility between public and private sectors for funding and delivering formal and regulated services (Meyers and Gornick, 2003; Lloyd and Penn, 2012; Gambaro et al, 2014). We know less about how policy mechanisms and funding structures can support the use of different types of ECEC, especially informal and unregulated care, including care in the child's home. The dynamic between these different types of services matter because, as Jenson and Sineau indicate, it is the ‘details of the services, the eligibility rules and forms of delivery’ that have an impact on the types of services accessible to families, and the quality of these services (2001, p 5). Government involvement in the funding, delivery and regulation of ECEC affects families’ early education and care options. Families’ ECEC decisions about whether to use formal/informal and regulated/unregulated care are therefore shaped by individual and socially embedded preferences (Vincent and Ball, 2006), but also by government policy structures and supports. As discussed in the previous chapters, policy developments and government’ positions towards in-home childcare have evolved alongside broader shifts in ECEC policy, which have included a shift from direct service delivery towards a marketised service environment led by demand-side funding. Funding and subsidies available for families using in-home childcare may or may not be contingent on services and individual providers meeting specific standards or regulations.
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 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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.
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".