Scalability and Heterogeneity: Targeting Interventions to Maximize Social Impact
Bibliographic record
Abstract
Social scientists have begun to recognize that planning for individuals’ heterogeneous needs is essential to successful behavior change efforts. However, there is still limited evidence about exactly where the challenges in scaling interventions arise and how to plan for and address these challenges. One notable pitfall in previous attempts is a one-size-fits-all intervention approach. This approach does not account for context-related and individual-level heterogeneity, and in management settings, fails to centralize the worker in designing behavior change interventions. This session contributes to the critical conversation of leveraging research insights for broad societal impact by assessing challenges and opportunities in scaling behavioral interventions broadly. We contribute a specific focus on heterogeneity based on individuals' motivations to engage in the activity, putting the user – in many cases, the worker – front and center. Challenges to Translating and Scaling Behavioural Interventions Author: Dilip Soman; U. of Toronto, Rotman School of Management Targeting Interventions Based on Baseline Motivation Increases Vaccine Author: Ilana Brody; UCLA Anderson School of Management Author: Hengchen Dai; UCLA Anderson School of Management Author: Silvia Saccardo; Carnegie Mellon U. - Department of Social and Decision Sciences Framing Charitable Giving as a Teachable Moment Can Increase the Generosity of Parents Author: Ashley Whillans; Harvard Business School Author: Christopher Bryan; McCombs School of Business, U. of Texas at Austin Author: Elizabeth Dunn; U. of British Columbia Scaling Behavioral Interventions to Reduce Student Absenteeism: Building “OPOWER For Absenteeism” Author: Todd Rogers; Harvard U. Author: Avi Feller; U. of California, Berkeley
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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 | 0.072 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".