How does framing influence preference for multiple solutions to societal problems?
Bibliographic record
Abstract
Solutions to environmental and social problems are often framed in dichotomous ways, which can be counterproductive. Instead, multiple solutions are often needed to fully address these problems. Here we examine how framing influences people's preference for multiple solutions. In a pre-registered experiment, participants (N = 1,432) were randomly assigned to one of four framing conditions. In the first three conditions, participants were presented with a series of eight problems, each framed with multiple causes, multiple impacts, or multiple solutions to the problem. The control condition did not present any framing information. Participants indicated their preferred solution, perceived severity and urgency of the problem, and their dichotomous thinking tendency. Pre-registered analyses showed that none of the three frames had a significant impact on preference for multiple solutions, perceived severity, perceived urgency, or dichotomous thinking. However, exploratory analyses showed that perceived severity and urgency of the problem were positively correlated with people's preference for multiple solutions, while dichotomous thinking was negatively correlated. These findings showed no demonstrable impact of framing on multi-solution preference. Future interventions should focus on addressing perceived severity and urgency, or decreasing dichotomous thinking to encourage people to adopt multiple solutions to address complex environmental and social problems.
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 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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".