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Record W3096735772 · doi:10.1080/01596306.2020.1843114

Dramatising the shock of the new: using Arts-based embodied pedagogies to teach life skills

2020· article· en· W3096735772 on OpenAlexaff
Sheila Robbie, Bernie Warren

Bibliographic record

VenueDiscourse Studies in the Cultural Politics of Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsSociologyAestheticsEmpathyPsychologySocial psychology

Abstract

fetched live from OpenAlex

The digital economy and the global pandemic, together with the effects of climate change, have taken a human toll affecting the pace of everyday life, creating an exponential increase in anxiety and stress related diseases. Today’s complex, globalised world creates a need to challenge and reconceptualise educational priorities. In an increasingly polarised world of beliefs and values, with a rise in populism and nationalism, empathy is essential. In order to function successfully, one needs to know who people are, and the whys and wherefores of their actions and beliefs. This article focuses on how the Arts and humanities can teach a new generation of life skills necessitated by our globalised society and the socio-political aspects of immigration. It discusses how embodied pedagogies help develop self-awareness, emotional regulation and affective empathy and reduce stress. The authors present Arts-based pedagogies and educational strategies that bring history, cultures and beliefs alive and help counteract hatred, tribalism and racism and the illusion of ‘us and them.’ The article examines what these changes in pedagogy can offer to the discourse, both in the need to keep body/mind healthy and acquisition of new skills necessary for adapting to a changing global environment and cultural /political landscapes.

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 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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.144
GPT teacher head0.481
Teacher spread0.337 · 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 teacher head, 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

Citations3
Published2020
Admission routes1
Has abstractyes

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