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Record W7133389403

Impact of neoliberal-inspired policies on educators' professional identity in five countries: Visions for a better future

2024· other· en· W7133389403 on OpenAlexaboutno aff
Marg Rogers, Fabio Dovigo, Laura K. Doan, Khatuna Dolidze, Astrid Mus Rasmussen

Bibliographic record

VenueRUNE (Research UNE) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisVisionWork (physics)Identity (music)Government (linguistics)NeglectProfessional development
DOInot available

Abstract

fetched live from OpenAlex

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.673
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.007

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 head0.038
GPT teacher head0.468
Teacher spread0.430 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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