Hindsight bias in expert surveys: How democratic crises influence retrospective evaluations
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
Expert surveys provide a standardized way to access and synthesize specialized knowledge, thereby, enabling the analysis of a diverse range of concepts and contexts that might otherwise be difficult to approach systematically. However, while studies of public opinion have long argued that cognitive biases represent potential problems when it comes to the general population, less attention has been paid to similar issues among expert respondents. This study examines one form of cognitive bias, hindsight bias. Hindsight bias refers to the tendency to retrospectively exaggerate one’s foresight of a particular event. We argue that hindsight bias is a potential problem when it comes to retrospective evaluation due to the difficulty involved in separating our assessments of the pre-crisis period from the knowledge that a crisis occurred. Using disaggregated data from the Varieties of Democracy Project, we look for evidence of hindsight bias in coders’ evaluations of the periods that preceded major crises of democracy. We find that coder disagreement is significantly higher in pre-crisis scenarios than in our control group. Concerningly, despite this disagreement, coders remain similarly confident in their assessments. This represents a potential problem for those who seek to use these data to study democratic breakdowns and transitions.
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.
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 | 0.001 | 0.006 |
| 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 | 0.000 |
| 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