Impact of neoliberal-inspired policies on educators' professional identity in five countries: Visions for a better future
Bibliographic record
Abstract
Early childhood educators work in a highly regulated environment in many Western nations due to managerialism, the arm of neoliberal-inspired policies. Our international study of educators' work revealed insights into the impacts of these systems on children and educators. This chapter presents findings from the five countries involved in the study, namely, Australia, Canada, Georgia, Italy and Denmark. The findings reveal that neoliberal-inspired policies manifest in two ways: Either educators deal with the increased managerial regulation, or with the neglect of the sector in the pursuit of higher profits. When educators feel they are not able to adequately educate and support children, their professional identity is affected. This study uses a critical neoliberal framework and a mixed-method approach. The participants were educators with different qualifications and roles, working in various service types. Data analysis of the numerical data used cross-tabulation. Qualitative Data were analysed using thematic analysis. Despite the changes in identity, educators provided many ideas on the ways their government can assist their work so that they can concentrate on supporting children's learning through play. This study will be of interest to policymakers, educators and teacher educators.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".