Adapting Interventions to Culture Can Improve Effectiveness and Cost-Efficiency
Bibliographic record
Abstract
The last few decades have seen ambitious new mega-studies testing ways to change people's behavior for good. Studies with thousands of participants have tested the effectiveness of financial incentives and psychological nudges to encourage people to get vaccinated, go to the gym, and work harder. The emerging theme across these studies is that money tends to work better than nudges. Yet most of this research has been done in Western cultures. This paper reviews studies testing interventions outside of the West. Those studies often find that the power of money is smaller, with psychological nudges sometimes more cost-efficient than financial incentives. What's more, the messages that come with interventions tend to be more effective outside of Western cultures if they emphasize interdependence and connection to other people. In sum, new evidence is suggesting policymakers should be careful about exporting the lessons from mega-studies done in the West to cultures outside the West (and even groups within Western countries with different cultural norms, such as middle class versus working class Americans). Instead, new studies are pointing to ways to deliver interventions more effectively in non-Western cultures.
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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 | 0.020 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".