Motivated recall in the service of the economic system: The case of anthropogenic climate change.
Bibliographic record
Abstract
The contemporary political landscape is characterized by numerous divisive issues. Unlike many other issues, however, much of the disagreement about climate change centers not on how best to take action to address the problem, but on whether the problem exists at all. Psychological studies indicate that, to the extent that sustainability initiatives are seen as threatening to the socioeconomic system, individuals may downplay environmental problems in order to defend and protect the status quo. In the current research, participants were presented with scientific information about climate change and later asked to recall details of what they had learned. Individuals who were experimentally induced (Study 1) or dispositionally inclined (Studies 2 and 3) to justify the economic system misremembered the evidence to be less serious, and this was associated with increased skepticism. However, when high system justifiers were led to believe that the economy was in a recovery, they recalled climate change information to be more serious than did those assigned to a control condition. When low system justifiers were led to believe that the economy was in recession, they recalled the information to be less serious (Study 3). These findings suggest that because system justification can impact information processing, simply providing the public with scientific evidence may be insufficient to inspire action to mitigate climate change. However, linking environmental information to statements about the strength of the economic system may satiate system justification needs and break the psychological link between proenvironmental initiatives and economic risk. (PsycINFO Database Record
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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 | 0.004 | 0.035 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".