System Justification, hope, and support for a Just Transition
Bibliographic record
Abstract
The present research aims to test whether the psychological phenomenon known as system justification, which has previously been linked to general measures of pro-environmental behaviour and support for climate action (Feygina et al., 2010), can usefully contribute to our understanding of other important phenomena related to climate action, including: support for climate policy, belief in climate delay discourses, and support for a Just Transition. Data was collected using a cross-sectional online survey of adult (18+) residents of small to medium sized communities in Western Canada (n = 3,400). Participants were almost representative of these regions' populations, but not quite (there was an overrepresentation of older and more educated residents of these communities, and an underrepresentation of younger and less educated residents). The online survey included an experiment, in which participants from Alberta and BC (n = 2,527) were randomly assigned to a control or an experimental condition. Following Greenaway et al. (2016), Participants in the experimental condition were asked to write about an element of their life that made them hopeful, while participants in the control condition were only asked to write about their lives. All participants included in the experiment were then asked how much they supported a Just transition within Canada. We plan to test four hypotheses: H1: System justification will predict support for climate policies. H2: System justification will predict belief in climate delay discourses. H3: Proximity to the fossil fuel industry will moderate the relationship between system justification and support for a Just Transition (while controlling for experimental condition) H4: Experimental condition will moderate the relationship between system justification and support for a Just Transition
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".