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Record W2046526606 · doi:10.1080/0266736042000251808

Applying Emotional Intelligence: Exploring the Promoting Alternative Thinking Strategies curriculum

2004· article· en· W2046526606 on OpenAlexaff
Barbara Kelly, Julie Longbottom, Fay Potts, Jim Williamson

Bibliographic record

VenueEducational Psychology in Practice · 2004
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsCochrane
Fundersnot available
KeywordsPsychologyCurriculumEmotional intelligenceEmotional competenceCompetence (human resources)Class (philosophy)CognitionAction researchDevelopmental psychologyEthosPedagogyMathematics educationSocial psychology

Abstract

fetched live from OpenAlex

This paper describes a collaborative action research project in one primary school that arose from a mutual interest in applying the concept of “Emotional Intelligence”. It involves an exploratory qualitative study of the Promoting Alternative Thinking Strategies (PATHS) curriculum. This is an approach aimed at promoting emotional competence in children and young people. The PATHS curriculum was chosen because of its clear conceptualisation of emotion, its emphasis upon cognitive and developmental aspects and its research history. One class of 9 and 10 year olds took part in the project. Target children were selected from within this group for closer monitoring. The outcomes suggest that PATHS was rated very positively by class teachers, pupils and other staff involved in the project. Positive emotional, social and behavioural changes at a class and individual level were attributed to the effects of PATHS. Finally, the importance of developing a positive school ethos was highlighted as promoting these effects.

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)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.108
GPT teacher head0.455
Teacher spread0.347 · 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 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

Citations80
Published2004
Admission routes1
Has abstractyes

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