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Record W2980726049 · doi:10.26443/ijwpc.v6i2.204

Facilitating Social-Emotional Learning in the Workplace

2019· article· en· W2980726049 on OpenAlexvenueno aff
Duke Biber

Bibliographic record

VenueInternational Journal of Whole Person Care · 2019
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsSocial emotional learningVariety (cybernetics)PsychologyPromotion (chess)Medical educationPedagogyHealth promotionNursingMedicinePolitical sciencePublic health

Abstract

fetched live from OpenAlex

The purpose of this commentary is to explain the integration of social emotional learning in higher education with faculty and staff. The University of West Georgia has established an applied holistic wellness lab, the Wolf Wellness Lab, that aims to facilitate social emotional learning for faculty and staff. The Wolf Wellness Lab was founded upon the National Wellness Institute’s framework of holistic health, including emotional, occupational, spiritual, intellectual, social, and physical health promotion. The Wolf Wellness Lab provides a variety of education, services and trainings for faculty and staff that can serve as a model for other universities, businesses, and community centers to facilitate SEL. The Wolf Wellness Lab has helped create an identity of social emotional learning and overall wellness in the department, college, and university at large, and such an identity and culture are often needed for successful and long-term healthy change. This commentary will discuss specific resources provided for faculty and staff that promote a culture of wellness.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0060.011
Scholarly communication0.0050.005
Open science0.0020.008
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0060.001

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 head0.031
GPT teacher head0.347
Teacher spread0.316 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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