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Record W3035780583 · doi:10.1080/1350293x.2020.1783930

Exploring the foundation for Collaborative Governance to support early childhood in Nova Scotia

2020· article· en· W3035780583 on OpenAlexafffundabout
Jessie‐Lee D. McIsaac, Erin Kelly, Joan Turner, Sara Kirk

Bibliographic record

VenueEuropean Early Childhood Education Research Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsMount Saint Vincent UniversityDalhousie University
FundersCanada Research Chairs
KeywordsGeneral partnershipNova scotiaEarly childhoodEarly childhood educationCorporate governanceCollaborative governancePublic relationsFoundation (evidence)Service delivery frameworkService (business)Political scienceSociologyPedagogyPsychologyBusinessDevelopmental psychology

Abstract

fetched live from OpenAlex

Providing the best start for our youngest generation is a growing priority internationally. Integrated Service Delivery (ISD) offers an approach to address complex system challenges and improve access to early childhood public services for young children and their families. The purpose of this paper is to use the Integrative Framework for Collaborative Governance to reflect on the elements of collaboration that may have influenced the establishment and implementation of an early childhood ISD approach in Nova Scotia (Canada). The provision of adequate resources for partnership development, clarification of roles and formal agreements to enable commitment and the importance of building collaborative capacity among partners was found to be critical to develop a shared vision for ISD for early childhood. The Integrative Framework for Collaborative Governance provided a useful lens to consider some of the interactive components of ISD in the early childhood initiative and further, more in-depth study is warranted to determine which components are necessary for collaborative success.

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)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.761
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.249
GPT teacher head0.427
Teacher spread0.178 · 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.

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

Citations4
Published2020
Admission routes3
Has abstractyes

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