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Record W4220966508 · doi:10.18432/ari29624

An Inspirited Artistic Co-Inquiry with Raw Energy

2022· article· en· W4220966508 on OpenAlexaffvenue
Darlene St. Georges, Barbara Bickel

Bibliographic record

VenueArt/Research International A Transdisciplinary Journal · 2022
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsExperiential learningIntuitionAestheticsCo-creationPoetrySpace (punctuation)Energy (signal processing)SociologyEpistemologyVisual artsArtPedagogyComputer sciencePhilosophyLiteratureLinguistics

Abstract

fetched live from OpenAlex

This is the first article of an in-process, creation-centred research project exploring raw energy through the authors’ distinctive and complementary inquiry practices of creation-centred research (St. Georges, 2020, in press) and spontaneous creation-making (Bickel, 2020; Bickel & Fisher, 1993). Raw energy, as conceived, is experienced as spirit-in-motion in a process of manifestation—of making the invisible visible—and is rooted in an intra)inter-relational aesthetic. This creation-centred inquiry is a relational and animated approach to creating, inquiry, learning, unlearning, and teaching. It resists the colonial lens by virtue of exploring inner subjective space, relinquishing colloquial aesthetic constraints, and enveloping a sacred space in which to restore, heal, and decolonize the imagination. Led by breath)spirit, touch, intuition, experiential and conversational exchanges, and compassionate relationships, creative lifeforce is activated to forge new ways of knowing—moving toward the extraordinary. This article engages with theoretical and explanatory text, visual and poetic storying, and interactive breath that invites the reader into this inquiring journey.

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)0.007
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 score0.010
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0080.029
Scholarly communication0.0100.006
Open science0.0010.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.129
GPT teacher head0.463
Teacher spread0.334 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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