When expert predictions fail
Bibliographic record
Abstract
We examine the opportunities and challenges of expert judgment in social sciences, scrutinizing the way social scientists make predictions. While social scientists show above chance accuracy in predicting lab-based phenomena, they often struggle to predict real-world societal changes. We argue that most causal models used in social sciences are oversimplified, confuse levels of analysis to which a model applies, misalign nature of the models with the nature of the phenomena, and fail to consider factors beyond the scientist’s pet theory. Taking cues from physical sciences and meteorology, we advocate for an approach that integrates broad foundational models with context-specific time series data. We call for a shift in the social sciences towards more precise, daring predictions, and greater intellectual humility.
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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 | 0.055 | 0.327 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".