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Record W4283265573 · doi:10.29173/ijll16

The Role of School Leaders in Promoting Successful Home–School Partnerships in Singapore’s Special Education Schools

2022· article· en· W4283265573 on OpenAlexaff
Marilyn Ang, Brenda Spencer

Bibliographic record

VenueInternational Journal for Leadership in Learning · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHome schoolContext (archaeology)Public relationsEducational leadershipGeneral partnershipQualitative researchPedagogyPerceptionSpecial educationPsychologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

Effective home–school partnerships are deemed highly important for the academic success and well-being of students with disabilities (Beveridge, 2005; Blue-Banning et al., 2004; Shelden et al., 2010). Findings of a qualitative case study of parents’ perspectives about the relationship between the home and Singapore’s special education (SPED) schools revealed that school leadership is a key contributor to positive and productive partnerships. Specifically, participants focused on the importance of leaders being welcoming and approachable, practicing strong communication skills and strategies, and having positive perceptions about parents and their role in the school. These findings suggest that school leaders who trust parents and can foster trust in their leadership can be key players in promoting positive relationships for successful home–school partnerships. While the study was conducted in Singapore, the findings are transferable to any K-12 education context, as they offer school leaders insights on how to foster a successful collaboration with the families.

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.002
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.169
GPT teacher head0.394
Teacher spread0.225 · 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 designQualitative
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 routes1
Has abstractyes

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