Becoming European (Legally): Unpacking the Self-Portrait of the EU Legal Order in the Pre-Accession Case-Law Dossiers
Bibliographic record
Abstract
With sadness we note the passing of Theodore Eisenberg .The entire empirical legal scholarship community lost a gracious colleague, an intellectual giant, and a true pioneer in empirical legal studies.Ted often is referred to as the "grandfather of empirical legal studies," a title he wore with no small dose of humble embarrassment, but a title that he richly deserved.He was irrepressibly curious, and in the service of that curiosity he respected the value of data, and, when collected properly, data's even handed and apolitical nature.As Ted told the Cornell Law Forum in 2008, "I like to let the data tell their own story, not try to superimpose one.As in any good scholarship, you check your assumptions.If you don't have the real facts, people will make them up or follow the headlines."Aside from voluminous contributions to ELS through his own scholarship and his many collaborations (he authored or coauthored over 125 scholarly articles and wrote or edited over 20 books or chapters in books), Ted helped build an institutional structure that serves as a vital foundation for the field's continued vigor.In addition to his pivotal role in founding the Journal of Empirical Legal Studies as an outlet for the best of empirical legal scholarship, Ted was part of the initial group of scholars, along with Jennifer Arlen, Bernie Black, Michael Heise, and Geoffrey Miller, who established the Society for Empirical Legal Studies.The Conference on Empirical Legal Studies, one of SELS' primary initiatives, brings together scholars from multiple disciplines, to explore the many legal questions for which the careful analysis of data may shed light.As a testament to CELS' vitality, from its successful start in 2006 at the University of Texas at Austin School of Law, past CELS law school hosts include NYU, Cornell, USC, Yale, Northwestern, Stanford, and Penn.This pattern continues with Berkeley in 2014 and the announcement of Washington University in St. Louis as host for 2015.Ted's legacy and his professional accomplishments are legend indeed.But they are secondary to the quality and warmth of the man who produced them.Ted was a man of uncommon decency, whose fertile mind, generous spirit, and unadorned manner sat easily with the great force of his scholarly vision and scope.He is deeply missed.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.057 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| 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".