Expert predictions of societal change: Insights from the world after COVID project.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How do experts in human behavior think the world might change after the coronavirus (COVID-19) pandemic? What advice do they have for the postpandemic world? Is there a consensus on the most significant psychological and societal changes ahead? To answer these questions, we analyzed interviews from the World After COVID Project-reflections of more than 50 of the world's top behavioral and social science experts, including fellows of National Academies and presidents of major scientific societies. These experts independently shared their thoughts on possible psychological changes in society in the aftermath of the COVID-19 pandemic and provided recommendations how to respond to the new challenges and opportunities these shifts may bring. We distilled these predictions and suggestions via human-coded analyses and natural language processing techniques. In general, experts showed little overlap in their predictions, except for convergence on a set of social/societal themes (e.g., greater appreciation for social connection, increasing political conflict). Half of the experts approached their post-COVID predictions dialectically, highlighting both positive and negative features of the same domain of change, and many expressed uncertainty in their predictions. The project offers a time capsule of experts' predictions for the effects of the pandemic on a wide range of outcomes. We discuss the implications of heterogeneity in these predictions, the value of uncertainty and dialecticism in forecasting, and the value of balancing explanation with predictions in expert psychological judgment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it