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Record W4401656474 · doi:10.6007/ijarbss/v14-i8/22257

An Insight into the Financial Challenges and Sustainability of Private Early Childhood Education Centres in Malaysia

2024· article· en· W4401656474 on OpenAlexaff
Izawati Ngadni, Gurdip Kaur Saminder Singh

Bibliographic record

VenueInternational Journal of Academic Research in Business and Social Sciences · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsSustainabilityEconomic growthBusinessEarly childhood educationEarly childhoodFinanceEconomicsPsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

The financial sustainability of early childhood education (ECE) centres in Malaysia is a critical concern since these institutions play a pivotal role in the developmental stages of young children. The increasing cost of governing businesses today has also significantly impacted the operations of educational institutions such as private early childhood centres in various ways. Early childhood operators are today facing numerous challenges that impact their ability to maintain financial stability. The ultimate purpose of this study was to unravel the complex web of challenges faced by operators of early childhood education (ECE) centres in Malaysia, with a deep focus on understanding their financial sustainability strategies. The research used a comprehensive qualitative approach to explore the challenges faced as well as capture rich insights on the strategies used by ECE operators, shedding light on informed strategies and policies that can strengthen the financial base of ECE centres.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.363
Teacher spread0.326 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2024
Admission routes1
Has abstractyes

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