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Record W2145805894 · doi:10.5539/jel.v4n3p112

Family-School-Professionals Partnerships: An Action Research Program to Enhance the Social, Emotional, and Academic Resilience of Children at Risk

2015· article· en· W2145805894 on OpenAlexvenueno aff
Ηλίας Κουρκούτας, Theodoros Eleftherakis, Elena Vitalaki, Angie Hart

Bibliographic record

VenueJournal of Education and Learning · 2015
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
FundersEconomic and Social Research CouncilArts and Humanities Research Council
KeywordsMediationPsychologyInclusion (mineral)Set (abstract data type)Psychological resilienceIntervention (counseling)Action researchAction (physics)Process (computing)PedagogyMedical educationMathematics educationSocial psychologySociologyMedicineComputer science

Abstract

fetched live from OpenAlex

This paper presents an action research program (note 1) which was designed to support parents and primary school teachers, with the mediation of school professionals in order to enable them facilitate the school inclusion of at risk students or those with special educational needs. The aims, the organization process, and the implementation of the program, as well as its theoretical and practical aspects/components, are presented. Resilience and inclusive education are the key theoretical frameworks informing this paper. These both advocate parent-teacher-professional partnerships to promote a “holding school environment” and support children with difficulties to avoid exclusion. An action research methodology was chosen in order to set up this program with the aim of enabling teachers and parents to be more “resilient” and “inclusive” towards children with special difficulties. The evaluation of the program showed that teachers and parents viewed this model of intervention very positively and gained significant knowledge of practices related to their respective role.

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)0.002
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 score0.140
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.293
GPT teacher head0.578
Teacher spread0.285 · 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

Citations13
Published2015
Admission routes1
Has abstractyes

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