MétaCan
Menu
Back to cohort
Record W7018724101

Encountering a stranger during a forest walk

2018· dissertation· en· W7018724101 on OpenAlexaboutno aff

Bibliographic record

VenueLeiden Repository (Leiden University) · 2018
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsAffect (linguistics)PerceptionScale (ratio)CognitionRating scaleStress (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

The primary purpose of this study is to investigate the effects of encountering another person during a forest walk and it's impacts on restoration.The MIST (Montreal Imaging Stress Task) was employed to create cognitive and physiological stress in 82 students.Three different conditions either simulated an encounter with a stranger, an encounter with an acquaintance or a situation with no encounter.During the whole procedure the heart rate was assessed.The PANAS (Positive and Negative Affect Scale) evaluated participants' individual perception of the situation as well as several self-report measurements evaluated the experience of restoration and contact experience and the attitude towards the walk.Results of the PANAS revealed a higher rating of the positive contact scale in the stranger condition than in the no-encounter condition as well as in the acquaintance condition.Results of the analysed heart rate change revealed that participants of the acquaintance condition underwent a declined level of restoration compared to participants of the stranger condition.Summing up, these findings show that participants of the acquaintance condition evaluated their situation less positive and restored worse than participants of the stranger condition.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.192
Teacher spread0.186 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueLeiden Repository (Leiden University)Same topicUrban Green Space and HealthFrench-language works237,207