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Record W3180548340

Provincial Differences in Environmental Cognitive Alternatives and Activist Behaviour

2021· article· en· W3180548340 on OpenAlexaboutno aff
Kyle Travis Mclellan

Bibliographic record

VenueSFU Undergraduate Research Symposium Journal · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionPsychologyAffect (linguistics)Sample (material)Identification (biology)Exploratory researchSocioeconomicsGeographySocial psychologySociologySocial scienceEcology
DOInot available

Abstract

fetched live from OpenAlex

Previous research has found that access to environmental cognitive alternatives (the ability to imagine a sustainable relationship with nature) predicts activist behaviour in a representative Canadian sample. Canada is a place with considerable variations in social norms, however, which affect the ability to engage in cognitive alternatives. Using a sample of residents of BC (N = 149), and residents of Alberta (N = 110), I compare scores on a measure of environmental cognitive alternatives between the two provinces. Additionally, I look at the relationship between these cognitive alternative scores, and scores on measures of environmental activist identification, willingness to engage in environmental activist behaviour, and willingness to engage in pro-environmental consumption. Results show that there is no significant difference in ability to engage in environmental cognitive alternatives between provinces, but that there is a strong relationship between engaging in cognitive alternatives and activist behaviours. When comparing both provinces, access to cognitive alternatives more strongly predicts activist identification and activist behaviour in Alberta than in BC. A further exploratory analysis using residents of Ontario (N = 467) and Quebec (N = 138) again found that there was a strong relationship between cognitive alternatives and activist identity and behaviour in these two provinces, but that the relationship remained strongest for residents of Alberta.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.868

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.326
Teacher spread0.299 · 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 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
Published2021
Admission routes1
Has abstractyes

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