Hastings Law News 2000 Vol.2 Iss.3
Bibliographic record
Abstract
IfHastings Trial Team members have learned anything this year, it's the valucofagood cross-examination.In fact, the cases litigated during the team's two most recent competitions have turned on the litigators' skill al extracting infonnalion from hostile witnesses.But an appreciation of impeachment is hardly the only thing that Trial Team members have picked up: These days, the four students on the squad can also produce greal cross-as well as terrifically effective directs, openings and summations.That capability is the result oftwo semesters of intensive training, as well as participation in three of the country's most prestigious national litigation contests.All of the team's members have an on-going interest in litigation.Once the Bar exam is out of the way, team captain Mcchelle Ayers wil l begin work as an Alameda County prosecutor.Jason Helsel has been hired bya San Diego finn specializing in criminal trial work; and Ray Mueller, also a3-L, insists that his principal career aim is to be a top--flight litigator.Tim O'Connor has signed on for a post-graduation job that doesn' t portend much trial work; on the other hand,
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.058 | 0.015 |
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; both teacher heads agree on what is shown here.
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".