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Record W2612070576 · doi:10.7202/1039632ar

Toward a Sustainable Sense of Self in Teaching and Teacher Education: Sustainable Happiness and Well-Being Through Mindfulness

2017· article· en· W2612070576 on OpenAlexaffvenue
Darlene Kyte

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2017
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsCape Breton University
Fundersnot available
KeywordsMindfulnessHappinessMeditationContext (archaeology)Well-beingPsychologyPedagogySustainabilitySociologySocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

This paper examines the effects of mindfulness practice throughpranayama,hatha yoga, and meditation on practicing teachers in an inner city school to determine if practices of this nature could contribute to sustainable sense of self, sustainable happiness, and well-being for practicing teachers and pre-service teachers. Furthermore, mindfulness practice is discussed in the context of new pedagogies that are contributing to the transformation of teachers, students, and education. This inquiry has demonstrated that this process not only offers the practitioner serenity, awareness, peace, and well-being: but the benefits of the practice move far beyond the personal environment of those practicing teachers and into the classroom and school community providing opportunities for transformation; growing sustainable happiness and well-being.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0000.002
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 head0.153
GPT teacher head0.423
Teacher spread0.270 · 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 designTheoretical or conceptual
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

Citations21
Published2017
Admission routes2
Has abstractyes

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