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Record W2943907678 · doi:10.1080/13504622.2019.1608425

Songwriting for nature: increasing nature connection and well-being through musical creativity

2019· article· en· W2943907678 on OpenAlexafffund
Katherine D. Arbuthnott, Glenn C. Sutter

Bibliographic record

VenueEnvironmental Education Research · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsRoyal Saskatchewan MuseumCampion CollegeUniversity of Regina
FundersEnvironment CanadaUniversity of Regina
KeywordsCreativitySocial connectednessNatural (archaeology)PsychologyMusicalMoodMathematics educationPedagogySocial psychologyGeographyVisual artsArt

Abstract

fetched live from OpenAlex

We conducted two field studies to examine whether songwriting retreats held in a natural setting would increase nature connectedness, as well as improve emotional well-being and performance on a creative reasoning task. In the first study, high school students took part in songwriting instruction either in their school or at a national wildlife conservation area. Results showed that the natural setting uniquely increased nature connection, while both settings improved mood and creative reasoning performance. In the second study, we observed similar effects for adults who attended songwriting retreats at the same conservation area and compared this to results from a science-based workshop in another remote natural area to assess whether nature connection effects were entirely attributable to nature contact. The findings of these studies imply that both musical creativity and outdoor education experiences improve well-being, while outdoor education also increases nature connectedness, and songwriting also increases creative reasoning.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.355
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.

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

Citations42
Published2019
Admission routes2
Has abstractyes

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