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An Integral View of Mindfulness Practices and the Perception of Challenge Within a High School Setting

2018· book-chapter· en· W2915032777 on OpenAlexaff

Bibliographic record

VenueAdvances in educational technologies and instructional design book series · 2018
Typebook-chapter
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMindfulnessViewpointsPerceptionPsychologySituatedEthnographyFocus groupPedagogySocial psychologyPsychotherapistSociologyComputer science

Abstract

fetched live from OpenAlex

The purpose of this chapter was to examine how mindfulness-based strategies are taught within four different classroom settings in a large urban high school and how they impact students' perceptions of challenge. Two different approaches toward mindfulness training were represented in the four classrooms: the first derived from an explicit, outcomes-based approach within a Yoga class setting with a focus on awareness of personal experience; the other was embedded and implicitly connected to the subject discipline of natural science with a focus on situated being. Integral methodological pluralism (IMP) was used to gather data from multiple viewpoints: phenomenological interviews, structural analysis of language frequency and comparisons, ethnographic observations, and hermeneutic interviews. Integral theory was used to analyze the data and identify the individual and cultural themes. Systemic influences are discussed in connection with these findings, and implications for implementation of mindfulness in relation to perception of challenge are explored.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.329
Teacher spread0.302 · 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 designQualitative
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

Citations1
Published2018
Admission routes1
Has abstractyes

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