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A no-fault proposal in the early 1990's failed because some legislators demanded provisions that addressed economic inequality, like equalization of lawyers' fees, Ms. Weinstein said.
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Lots of lawyers are involved.
On the other hand, every losing lawyer divide its (unit) prestige equally, so if many lawyers win against one lawyer their weight will be smaller in comparison to winning with a smaller set of lawyers.
Using the cases database, we build network of lawyers nodes correspond to lawyers and a directed edge from lawyer A to lawyer B indicates that A "lost" one or more cases to B. The idea is that when a layer loses a case to another lawyer, for former gives importance to the latter.
We are afraid of lawyers".
Moreover, edge have weights to capture the number of lawyers in a given case and the number of times two lawyers have faced each other.
Note that the final graph is not necessarily bipartite, as lawyers win and lose cases, and participate with different sets of lawyers in different cases.
On the other hand, we have lawyers that participated in more than thousand cases, indicating that the participation of lawyers in legal cases follows a heavy-tailed distribution.
The edge weight captures the importance of the lawyer in winning the case: a larger w yields less importance to a given lawyer in the winning set; a larger l (number of lawyers that lost the case) generates more edges to a given lawyer in the winning set.
Figure 3 also shows that the participation of lawyers in settlements is larger.
We start by characterizing the participation of lawyers in different cases.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com