MétaCan
Menu
Back to cohort
Record W2602892021 · doi:10.1027/1864-1105/a000213

The Impact of Immersive Technology on Nature Relatedness and Pro-Environmental Behavior

2017· article· en· W2602892021 on OpenAlex
Monica Soliman, Johanna Peetz, Mariya Davydenko

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Media Psychology Theories Methods and Applications · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsychologyNatural (archaeology)Human–computer interactionSocial psychologyCognitive psychologyComputer science

Abstract

fetched live from OpenAlex

Abstract. Those who feel connected to nature tend to be more likely to engage in pro-environmental behavior. How can this connection with nature be created? We examined whether viewing nature-related videos – specifically, the immersiveness of the technological devices used to display these videos – can enhance connection with nature and increase pro-environmental behavior. Participants watched videos of either natural or built environments through a head-mounted display (immersive technology) or a regular computer screen. We predicted that watching a nature video would enhance nature relatedness and pro-environmental behaviors, particularly when presented with immersive technology than with a traditional computer monitor. There was limited support for the hypotheses; watching the nature video significantly enhanced nature relatedness but not pro-environmental behaviors. The type of technology used did not influence the effect of the videos.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.672

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.426
Teacher spread0.409 · 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