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Record W2766726866 · doi:10.1089/cyber.2017.0249

Spiritual and Affective Responses to a Physical Church and Corresponding Virtual Model

2017· article· en· W2766726866 on OpenAlexaff
Matt Murdoch, Jim Davies

Bibliographic record

VenueCyberpsychology Behavior and Social Networking · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsCarleton University
Fundersnot available
KeywordsFeelingPsychologyFidelitySpiritualitySocial psychologyExploratory researchComputer scienceSociology

Abstract

fetched live from OpenAlex

Architectural and psychological theories posit that built environments have the potential to elicit complex psychological responses. However, few researchers have seriously explored this potential. Given the increasing importance and fidelity of virtual worlds, such research should explore whether virtual models of built environments are also capable of eliciting complex psychological responses. The goal of this study was to test these hypotheses, using a church, a corresponding virtual model, and an inclusive measure of state spirituality ("spiritual feelings"). Participants (n = 33) explored a physical church and corresponding virtual model, completing a measure of spiritual feelings after exploring the outside and inside of each version of the church. Using spiritual feelings after exploring the outside of the church as a baseline measure, change in state spirituality was assessed by taking the difference between spiritual feelings after exploring the inside and outside of the church (inside-outside) for both models. Although this change was greater in response to the physical church, there was no significant difference between the two models in eliciting such change in spiritual feelings. Despite the limitations of this exploratory study, these findings indicate that both built environments and corresponding virtual models are capable of evoking complex psychological responses.

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 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 score0.149
Threshold uncertainty score1.000

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 studies0.0020.001
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 head0.044
GPT teacher head0.355
Teacher spread0.311 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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