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Record W2191052260 · doi:10.3390/socsci4041316

Intersectoral Mobilization in Child Development: An Outcome Assessment of the Survey of the School Readiness of Montreal Children

2015· article· en· W2191052260 on OpenAlexafffundabout
Isabelle Laurin, Angèle Bilodeau, Nadia Giguère, Louise Potvin

Bibliographic record

VenueSocial Sciences · 2015
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsMobilizationEarly childhoodAppealLiteracyPolitical scienceCollective actionAction (physics)Child developmentPublic relationsPsychologyEconomic growthPedagogyDevelopmental psychologyEconomics

Abstract

fetched live from OpenAlex

In 2006, the department of public health in Montreal, Quebec, Canada, conducted the Survey of the School Readiness of Montreal Children. After unveiling the results in February 2008, it launched an appeal for intersectoral mobilization. This article documents the chain of events in the collective decision-making process that fostered ownership of the survey results and involvement in action. It also documents the impacts of those findings on intersectoral action and the organization of early childhood services four years later. The results show that the survey served as a catalyst for intersectoral action as reflected in the increased size and strength of the actor network and the formalization of the highly-anticipated collaboration between school and early childhood networks. Actors have made abundant use of survey results in planning and justifying the continuation of projects or implementation of new ones. A notable outcome, in all territories, has been the development of both transition-to-kindergarten tools and literacy activities. The portrait drawn by the research raises significant issues for public planning while serving as a reminder of the importance of intersectoral mobilization in providing support for multiple trajectories of child preschool development.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.225
GPT teacher head0.508
Teacher spread0.284 · 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

Citations8
Published2015
Admission routes3
Has abstractyes

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