Working the Crowd: Citizen Forecasting, Sophistication and Diversity in Canadian Federal and Provincial Elections
Bibliographic record
Abstract
Abstract According to the “miracle of aggregation” principle, in the absence of systematic biases, errors in individual judgments within a population should cancel each other out and lead to a correct decision at the aggregate level. This article explores potential individual- and group-level correlates of the accuracy of citizens’ electoral expectations and investigates how potential markers of political sophistication—namely, educational attainment and political interest—could be used to improve upon the raw aggregation of citizens’ forecasts using massive survey datasets collected during six Canadian national and provincial election campaigns between 2011 and 2022 (n = 279,003). We find that while educational attainment and interest increase the probability of a correct forecast at the individual level, delegating the forecasting task based on these variables does not necessarily lead to improvements in the accuracy of aggregate-level predictions. At the group level, we fail to uncover any evidence that sociological or informational diversity increases forecasting accuracy.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".