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Record W4287831671 · doi:10.1017/bpp.2022.18

Information effects on parental choices for early childhood education and care

2022· article· en· W4287831671 on OpenAlexaffabout
Samantha Burns, Adrienne Davidson, Linda A. White, Delaine Hampton, Michal Perlman

Bibliographic record

VenueBehavioural Public Policy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsEarly childhood educationPsychologyTest (biology)Child careSample (material)Conjoint analysisSocial psychologyMedical educationDevelopmental psychologyNursingMedicineEconomics

Abstract

fetched live from OpenAlex

Abstract Existing research demonstrates that parents are poorly informed consumers of early childhood education and care (ECEC) services. Choosing such services is a complex process shaped by a combination of logistical limitations (e.g., cost/location), informational barriers and ideas about what the goal of care should be (e.g., education of young children or provision of an environment that feels like home). Experimental studies have also demonstrated that when study participants are informed of the importance of a specific decision, they engage in more complex decision-making. In this article, we test whether providing parents with information about the regulatory stringency of ECEC options available influences their choices regarding ECEC. A conjoint survey designed to capture quasi-behavioural choices for ECEC services was completed by 682 parents. Before engaging with the survey, participants were randomly assigned into either a control group or a treatment group that informed them about the stringency of oversight regarding ECEC options available in the province of Ontario, Canada. Receiving information did not meaningfully change the choices of the entire sample. However, a subgroup analysis revealed an important information effect on parent decisions for lower income/lower-education parents.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.305
Teacher spread0.288 · 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; a candidate call from one teacher head, not a consensus.

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

Citations4
Published2022
Admission routes2
Has abstractyes

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