Bibliographic record
Abstract
The UND School of Law is proud to welcome the students that comprise the law Class of 2014. Eighty-four first-year law students began their academic careers on August 15, 2011 with a special banquet and pinning ceremony. North Dakota Supreme Court Justice Mary Maring welcomed the class to campus, discussed with them the importance of professionalism and led them in reciting their Oath of Professionalism. Each member received a special Class of 2014 pin symbolizing the beginning of their academic journey to earn the Juris Doctor degree. Their first week on campus consisted of a combination of orientation activities and the completion of the introductory week of their Lawering Skills class. The profile of the class is listed below. This group brings a variety of academic, personal and professional experiences with them, including students who have taught English in China, served in the military, competed in the Miss USA competition, performed overseas with music groups, and competed in (and won) a game show. Profile of the Class of 2014 Average Age - 28 GPA 25% - 2.93 GPA 75% - 3.61 Median - 3.37 Avg Index - 2.8 Average LSAT - 150.5 LSAT 25% - 148 LSAT 75% - 154 LSAT Median - 151 Men - 43 Women - 41 States - 17 ND - 48 (57%) MN - 13 (15%) Countries - 1 Canada - 5 (6%) Undergrad Schools - 41
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.261 | 0.117 |
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".