Impacts of managerial systems on early educators’ job satisfaction in five countries
Bibliographic record
Abstract
The work of early childhood educators is conducted in highly regulated environments in many Western nations. This is due to managerialism, the right arm of neoliberal-inspired policies. To explore educators' work within these contexts, our international study highlights the impacts of these systems on educators and the children they teach. This paper presents findings from five countries, namely, Australia, Canada, Denmark, Georgia and Italy. The results reveal the experiences of educators in these countries, impacted by neoliberal-inspired policies that are manifested in two different ways. They are dealing with increased managerial regulation or with the neglect of the sector in the pursuit of higher profits. Educators' job satisfaction is impacted when they perceive they are not able to adequately educate and support children due to these constraints. Using a critical neoliberal framework, we employed a mixed-method approach. The participants were educators with various roles and qualifications in a variety of service types. To analyse the numerical/closed answer data we used cross tabulation. Thematic analysis was used to analyse the qualitative data. Despite their difficulties, educators provided many ideas on the ways their government can better support their work so that they can focus on supporting children's learning through play. This study will be of interest to researchers, educators, policymakers 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".