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Record W4396976887 · doi:10.1080/13504622.2024.2350675

Aesthetic flux: inquiring into the sensuous dynamics of children, matter and environments with a more-than-human lens

2024· article· en· W4396976887 on OpenAlexaff
Jenny Renlund, Kristiina Kumpulainen, Jenny Byman, Chin Chin Wong, Sara Sintonen

Bibliographic record

VenueEnvironmental Education Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of British Columbia
FundersMaj ja Tor Nesslingin SäätiöAcademy of Finland
KeywordsLens (geology)Dynamics (music)Environmental educationPsychologyAestheticsEcologyPedagogyPhilosophyPhysicsBiologyOptics

Abstract

fetched live from OpenAlex

Although sensuous and embodied engagement is an integral part of child–environment relationalities, the intersections of aesthetics, children and environments remain scarcely addressed. As a response, this study develops a concept of ‘aesthetic flux’ to delve into the sensuous dynamics of matter and bodies in the context of a storying workshop in a forest with first graders in a Finnish primary school. An arts-based, post-qualitative methodology guided our analysis of video recordings from the workshop, resulting in visual-sonic montages that draw attention to the intense movements and sounds of children, soap bubbles, air, a research camera and trees. Thinking through the concept of aesthetic flux, our study experiments with the abundance, indeterminacy and potentiality of sensuous dynamics where bodies (human and otherwise) become together and linger. Thus, our study reconfigures aesthetics as a creative and unpredictable force that materialises in both embodied and conceptual ways in environmental education and research with children.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.367
Teacher spread0.347 · 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.

Study designObservational
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

Citations4
Published2024
Admission routes1
Has abstractyes

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