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Record W2897316777

Organisational, relational and reflective competences in ECEC

2018· article· en· W2897316777 on OpenAlexaboutno aff
Florence Pirard

Bibliographic record

VenueOpen Repository and Bibliography (University of Liège) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge managementPedagogyReflection (computer programming)BusinessSociologyPublic relationsPolitical scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

Literature indicates that working with young children and their families should be nowadays a central dimension in the curricula of the ECEC services (European Commission, 2014, 0CDE 2015) and requires core competences that should be developed in the initial training and sustained in a 'competent system’ (Urban, al 2011). The analysis of some experiences in several countries underlines the importance of a broad, multilevel and contextualised perspective (Vandenbroeck, 2016) which engages professionals, their leaders, their teams and other stakeholders in learning professional communities (Sharmahd, 2017). All these results show the importance of relational, interpersonal and reflective competences beyond technical skills. Three action research projects on initial training and professional development have been carried out in Wallonia Brussels Federation (WBF) since 2011. In these, 150 practitioners, trainers, teachers, political heads and researchers from France, Flanders, England, Sweden and Quebec were gathered. Four steps were set: analysis of the main programmes of initial training in WBF, discovery of the educational programmes of several countries known for their quality workforce, proposal of recommendations. The results underline six principles and three competences to reform initial training and continuing professional development. They recognise the practitioners in the field of ECEC as educational professionals of relationships and reflexivity.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.089
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
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 head0.040
GPT teacher head0.327
Teacher spread0.287 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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