Bibliographic record
Abstract
Is it hard being a lawyer? did you decide to be a lawyer? What's the most difficult case you've ever handled? These are all questions asked by high school students. Twice a year, I attend the Virtual School of Law at Vanderbilt University, through videoconferencing, with high schools throughout the United States and Canada. Usually, the audience consists of seniors in high school from five or six different schools in different cities. It gives these high school students an opportunity to ask questions and explore a possible career in the law. Of all these questions, perhaps the one by which I was most taken aback was a question asked by a senior from a high school in Texas. She asked, How do you deal with the stereotypes of lawyers, and how does that make you feel? I asked this student to repeat the question as I didn't understand it. She said something to the effect of, You know being compared to 'sharks' and not having an honest reputation and only interested in money and winning at any cost. Wow! I was a bit taken aback by this question. During the years that I've participated in this virtual program, the questions are similar to those above and the most personal they ever get is, How much money do you make? I was also surprised by the fact that a senior held this opinion of lawyers or perceived this stereotype of lawyers. I asked her if this was an opinion formed from personal experiences with attorneys. She responded that it was not, but it was only from what she heard other people saying about lawyers. While my initial reaction was a defensive one, I knew there was no way to win an argument with a high school senior with a hundred of her peers listening in and watching by video conference. Instead, I asked how much she knew of the situation in Iraq. I asked if she knew of Hammurabi and that one of the earliest legal systems originated in Iraq and the proud heritage the people of Iraq had through their legal systems. While I saw many heads nod at the mention of the Code of Hammurabi, I told them of a colonel in the Army JAG who was helping recreate a justice system in the post-Saddam Hussein Iraq. I told them of the frantic call received from an Iraqi judge whose wife and four small children were being fired upon in their home by insurgents in Baghdad. I told them of the young Army captain, a graduate of Cornell Law School, who instead of waiting on armored support to help rescue this judge and his family, jumped in an unarmored Humvee, raced through Baghdad, and broke through the fence surrounding the judge's home. He extracted the judge and his family, and through a hail of bullets and grenade explosions, he brought them to the safety of the Green Zone. When asked why he did this, the young captain, perhaps, reminiscent of General George Patton's response to why he forced the Third Army to save the besieged 101st Airborne in Bastogne in World War II, commented, A lawyer with that much passion for the law needed to be rescued--we couldn't afford to lose someone like him. …
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".