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Record W6904930399 · doi:10.14288/1.0444176

Transforming Perceptions : The Role of Perspective-Taking in the Canadian Policy Context

2024· article· en· W6904930399 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)EmpathyPerceptionPipeline (software)PoliticsRegulatory focus theoryFocus group

Abstract

fetched live from OpenAlex

This study investigates the role of empathy and perspective-taking in shaping policy support toward oil and gas pipelines in Canada, with an applicable focus on the Trans Mountain Pipeline (TMX) project. Conducted through an online survey experiment with 442 participants, the study examines the influence of a perspective-taking exercise on policy support and political behaviour. By prompting participants to imagine themselves in the situation of those directly impacted by Canadian pipeline policies, this research aims to assess the potential of empathy, induced through perspective-taking, to foster a more inclusive approach to policy evaluation. The results indicate that perspective-taking significantly decreased the likelihood of support for pipeline policies. However, it did not have a significant effect on participants' willingness to engage in actions like signing a petition against the TMX expansion to the Canadian government. The study further revealed that participants who underwent the perspective-taking exercise reported higher levels of emotional intensity (concern, sympathy, anger, activism), which in turn, significantly correlated with a decrease in policy support. In this context, emotional response is understood as an important mechanism between perspective-taking and changes in policy support, suggesting a need for further research on their impact on policy attitudes and behaviors.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.013
GPT teacher head0.247
Teacher spread0.233 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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