Notice bibliographique
Résumé
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. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,013 | 0,016 |
| Communication savante | 0,010 | 0,010 |
| Science ouverte | 0,002 | 0,009 |
| Intégrité de la recherche | 0,003 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».