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Record W2995597256 · doi:10.14288/1.0387190

The Impact of Environment on Climate Change Action

2019· article· en· W2995597256 on OpenAlexaboutno aff
Jessie Smith-Manchip, Grace Ng, Gabriel McKeown, Yoo Jung Kim, Kezia Reinata Cahyadi

Bibliographic record

VenuecIRcle (University of British Columbia) · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeAction (physics)Geology

Abstract

fetched live from OpenAlex

Our study explored the effect of one’s immediate environment on their likelihood of engaging in pro-environmental behaviour. Specifically we survey participants in three different locations across UBC Vancouver campus, two of these locations were defined as urban (one indoor and one outdoor), and one was defined as natural. When comparing the results from all three conditions there was no significant effect on petition signing behaviour. However, we did find a significant effect on petition signing between our indoor urban and natural environment. We hypothesise that the lack of an effect found at our outdoor urban condition may be due to the fact that it wasn’t perceived as urban by participants. Overall our research suggests that environmental context may influence one’s likelihood on engaging in pro-environmental behaviours, however, more research is needed to confirm or deny this, as well as to undercover the mechanisms by which this may work. Based on our results we suggest that people canvassing for petition signatures for environmental issues do so in a natural environment such as a park to increase efficiency. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.188
Teacher spread0.177 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

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