Empirisch-juridisch onderzoek in Nederland
Bibliographic record
Abstract
Empirical legal studies in The Netherlands Empirical Legal Studies (ELS) is research in which legal questions are answered using empirical research methods. Traditionally, lawyers conduct normative, non-empirical research. Lately the legal discipline is increasingly interested in ELS. It is argued that we need more ELS. This raises the question to what extent Dutch researchers and practitioners conduct and apply ELS. In this article, we investigate the state of affairs of ELS in the Netherlands. We look at three different areas: legal research, legal education and legal practice. The data we use are legal PhD theses, legal course material, legislative proposals, and questionnaire data from legal practitioners. The methods are a systematic review, a quantitative content analysis, and a questionnaire research. Our study on legal research shows that researchers do apply empirical methods, but mainly the researchers with an education in social science. Our study on legal education shows that lawyers receive hardly any training on empirical research methods. Finally, our research on legal practice shows that practitioners and legislators struggle to apply empirical legal research. We plead for investments to enhance the production and usage of ELS, to prevent wrongful judicial decision-making, to generate effective legislation, and to create scientific innovation.
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.
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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".