Job Announcement - Visiting Assistant Professor of Lawyering Skills.
Bibliographic record
Abstract
The University of North Dakota School of Law, in Grand Forks, ND, invites applications for a Visiting Assistant Professor of Lawyering Skills for a two- or three-year appointment commencing in Fall 2018. This is a full-time, non-tenure eligible faculty position with the primary responsibility of effective teaching. The successful candidate will teach two sections each semester (approximately 20 students per section) of Lawyering Skills I & II, the required first-year legal research, writing, and analysis courses. The successful candidate will join an autonomous yet collaborative program, in which faculty design their own syllabi, teaching materials, and assignments and supervise student teaching assistants. The successful candidate also will contribute to the School of Law’s curriculum and student success through additional teaching obligations determined based on the School of Law’s curricular and academic support needs and the successful candidate’s interests and skills, and may include academic support programming, intercession courses, skills team coaching, etc. The position offers an initial academic year contract that carries the possibility of re-appointment for up to three years based on satisfactory performance and curricular needs and contingent on administrative approval. The salary is $82,500 for a nine-month academic year appointment. The successful candidate will be eligible for financial support for expenses related to professional development efforts. Minimum Requirements/Qualifications J.D. degree from an ABA-accredited law school licensed member in good standing of the bar in any U.S. or Canadian jurisdiction two years of recent legal work experience (may include judicial clerkships) Please apply through our Human Resources Application Portal. The position is open until filled, with applications received by February 1, 2018 receiving priority consideration. The position will commence August 15, 2018. AA/EEO/Veterans/Disability Employer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.535 | 0.215 |
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; the direct Gemma label and the distilled Codex classifier 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".