Avoiding the Common Wisdom Fallacy: The Role of Social Sciences in Constitutional Adjudication
Bibliographic record
Abstract
More than one hundred years ago, the U.S. Supreme Court started to refer to social science evidence in its judgments. However, this has not resonated with many constitutional courts outside the United States, in particular in continental Europe. This contribution has a twofold aim. First, it tries to show that legal reasoning in constitutional law is often based on empirical assumptions so that there is a strong need for the use of social sciences. However, constitutional courts often lack the necessary expertise to deal with empirical questions. Therefore, I will discuss three potential strategies to make use of social science evidence. Judges can interpret social facts on their own, they can afford a margin of appreciation to the legislator, or they can defer the question to social science experts. It will be argued that none of these strategies is satisfactory so that courts will have to employ a combination of different strategies. In order to illustrate the argument, I will discuss decisions of different jurisdictions, including the United States, Canada, Germany and South Africa.
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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 | 0.177 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.019 | 0.127 |
| Scholarly communication | 0.021 | 0.039 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.021 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 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".