Can Lawyers Think Like Scientists?
Bibliographic record
Abstract
The Center for Ocean and Coastal Mapping/Joint Hydrographic Center at UNH (CCOM/JHC) has played a key role in collecting extended continental shelf data for the United States under the Law of the Sea Convention. Betsy Baker, a Vermont Law School professor, will talk about how law and science interact in that process, based on her time working with CCOM/JHC scientists on two USCGC Healy Arctic extended continental shelf mapping deployments in 2008 and 2009. She will draw connections between the origins of the LOS Convention and how the continental shelf is regulated today, and touch briefly on the limits of both science and law when it comes to addressing disasters like the Deepwater Horizon/BP Macondo spill. Presenter Bio Betsy Baker is an ssociate professor at the Vermont Law School and spent 2009-2010 as a Dickey Research Fellow at the Dartmouth College Institute of Arctic Studies. Her current research examines Canadian and U.S. federal-Inuit relations and their effect on environmental protection; means to improve access to the Arctic Ocean for scientific research (working with University of Alaska Fairbanks Geophysical Institute colleagues); and analyzing arctic offshore oil and gas regulatory regimes. She earned her J.D. at the University of Michigan her LL.M. and Dr. iur at Christian-Albrechts-Universität zu Kiel, Germany, where she was an Alexander von Humboldt Chancellors Fellow.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".