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Record W4417261125 · doi:10.1002/ijop.70146

The Influence of Deep Space and the Stars on Emotions

2025· article· en· W4417261125 on OpenAlexaff
Jason P. Martens, Mia Prokopetz, Kit Tomlinson

Bibliographic record

VenueInternational Journal of Psychology · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsCapilano University
Fundersnot available
KeywordsStarsDeep space explorationSpace (punctuation)Affect (linguistics)SkyNASA Deep Space NetworkFeeling

Abstract

fetched live from OpenAlex

Detailed photographs of deep space from the James Webb telescope are public, yet little is known about how such imagery might affect people. Using both face-to-face and online study designs, compared to exposure to photographs of urban environments, exposure to photographs of deep space and stars increased experiences of awe overall and also its 6 subfactors, and in particular vastness (e.g., "I felt in the presence of greatness") and accommodation ("I found it was hard to comprehend the experience in full"). Effects were generally larger for photographs of deep space than those of the stars. Mixed results were found on positive affect in general, with it sometimes increasing after exposure to deep space and the stars. No effects emerged on negative affect. Deep space and stars also led to higher ratings of pleasantness of the images, perceived restoration, and willingness to hang such photos in their room compared to urban photographs. Moderators were also assessed (i.e., feeling connected to the night sky and fear of the dark). Overall, results suggest that photographs of deep space from the James Webb telescope have similar, though not identical, effects as photographs of stars, both of which are generally more positive than urban photographs.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.359
Teacher spread0.335 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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