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Record W2280000806 · doi:10.1080/0305764x.2015.1125450

Establishing systemic social and emotional learning approaches in schools: a framework for schoolwide implementation

2016· article· en· W2280000806 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCambridge Journal of Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of British Columbia
FundersPennsylvania State UniversityRobert Wood Johnson Foundation
KeywordsBlueprintSocial emotional learningQuality (philosophy)PedagogyPsychologyAction (physics)Mathematics educationDevelopmental psychology

Abstract

fetched live from OpenAlex

Social and emotional learning (SEL) is a fundamental part of education. Incorporating high-quality SEL programming into day-to-day classroom and school practices has emerged as a main goal for many practitioners over the past decade. The present article overviews the current state of SEL research and practice, with a particular focus on the United States. The need for a model of SEL that goes beyond the classroom is illustrated, and a systemic approach to implementing SEL school-wide is introduced. It is argued that school-wide SEL maximises the benefits of SEL programming by becoming the organising framework for fostering students’ potential as scholars, community members, and citizens. Further, a Theory of Action (ToA) developed by the Collaborative for Academic, Social, and Emotional Learning (CASEL) is presented that serves as a blueprint for implementing systemic SEL in schools. Potential challenges and barriers involved in moving toward school-wide SEL implementation are considered and discussed.

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.

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)0.002
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 score0.272
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.065
GPT teacher head0.371
Teacher spread0.306 · 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