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Hamilton Court Judges, Research Paper Example

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Research Paper

Ratios of Reversal: A Probabilistic Analysis of U.S. appellate court decision

Introduction

Statistical analysis of judicial court decision provides the focus of query in the foregoing discussion. At present, probabilistic interpretation of legal fact finding is typically directed at evidentiary or quantified facts, but also to aggregate decision making outcomes as represented in this study. Risks attendant to interpretation of judicial procedure based upon mere belief do not serve to adequately represent the variance in logic models systems available to justices (Hillel, 2002). Model approaches to legal fact finding offer a range of methodologies toward precision in application of statutory rules. In the case of common law applications, judicial opinion is complicated by not only existing law, but the process oriented, previous court term decision toward resolution of later disputes. Contiguous decisions on cases adjudicated in appellate courts substantiate the best site for research in this area. ‘Next term’ case settlement by judges is quite explicit in common law countries like Australia, Canada, New Zealand, United Kingdom and United States (Moens, 2007). Cross breeding in global legal culture has also led to increased interest in ‘next term’ decision in civil law countries, as international law becomes more obtuse with legislative policies derived from precedent litigation, that is then often incorporated into national laws in those contexts.

As law becomes more enmeshed with changes to technologies and science in particular, the efficacy and prevalence of utilizing tools for decision might find advancement as justices now confront vast bodies of information and new knowledge formats, not to mention the implications of the number of lawsuits filed in response to everything from intellectual copyright on new innovations, to IT privacy invasion. The specialized expertise of Legal Analysts interested in term court decision may find reference in record (i.e. documentation) from the previous term, and are typically available for review in electronic form. Judicial opinion contains the elements, facts, policy issues and precedent laws informing the decision (Moens, 2007). The integration of economics and law is particularly promising in shared assessment of those records, as “algorithms for natural language processing and already traditional link-based ranking algorithms used by search engines stimulate research in this unexploited domain of legal documents” (Moens, 2007). Such studies are highly valuable, and the provenance of experts. The current study offers basic probabilistic analysis of enumerated judicial decision with the following results: 1) Probability of appeals by the courts; 2) Probability of Reversals by the courts; 3) Probability of appeal by Judge; 4) Probability of reversal by Judge; and 5) Probability of reversal given appeal by Judge. An Index of ranking by total cases for each Judge is also available for reference (Appendix).

Table 1

Court Probabilities Diposed Appeals Reversals Prob AP (%) Prob AP (.)
Common 43,945 1762 199 3.8 0.0383827
Domestic 30,499 106 17 0.3 0.0034615
Muni 108,464 500 104 0.5 0.0046058

Table 1: Probability of Appeals by Court

Table 2

Court Probabilities Diposed Appeals Reversals Prob RV  (%) Prob RV (.)
Common 43,945 1762 199 0.4 0.0043349
Domestic 30,499 106 17 0.06 0.0005551
Muni 108,464 500 104 0.1 0.000958588

Table 2: Probability of Reversals by Court

Table 3

   Judge Disposed Appealed Reversed Court Prob AP (%) Prob AP (.)
Fred Cartolano 3037 137 12 Common 4.3 0.0430006
Thomas Crush 3372 119 10 Common 3.4 0.0339902
Patrick Dinkelacker 1258 44 8 Common 3.3 0.0335877
Timothy Hogan 1954 60 7 Common 3.0 0.0296882
Robert Kraft 3138 127 7 Common 3.9 0.0388141
William Mathews 2264 91 18 Common 3.8 0.038348
William Morrissey 3032 121 22 Common 3.8 0.0381102
Norbert Nadel 2959 131 20 Common 4.2 0.0421221
Arthur Ney Jr. 3219 125 14 Common 3.8 0.0382496
Richard Niehaus 3353 137 16 Common 3.9 0.0390758
Thomas Nurre 3000 121 6 Common 3.9 0.0386952
John O’Connor 2969 129 12 Common 4.1 0.041479
Robert Ruehlman 3205 145 18 Common 4.3 0.430522
J. Howard Sundermann Jr. 955 60 10 Common 5.9 0.0585365
Ann Marie Tracey 3141 127 13 Common 3.9 0.00387077
Ralph Winkler 3089 88 6 Common 2.8 0.0276468
Penelope Cunningham 2729 7 1 Domestic 0.3 0.0025575
Patrick Dinkelacker 6001 19 4 Domestic 0.03 0.003154
Deborah Gaines 8799 48 9 Domestic 0.5 0.00542
Ronald Panioto 12970 32 3 Domestic 0.2 0.0024605
Mike Allen 6149 43 4 Muni 0.7 0.0069399
Nadine Allen 7812 34 6 Muni 0.4 0.0043301
Timothy Black 7954 41 6 Muni 0.5 0.0051243
David Davis 7736 43 5 Muni 0.4 0.0363175
Leslie Isaiah Gaines 5282 35 13 Muni 0.6 0.0063789
Karla Grady 5253 6 0 Muni 0.1 0.0011409
Deidra Hair 2532 5 0 Muni 0.2 0.0019708
Dennis Helmick 7900 29 5 Muni 0.4 0.0036551
Timothy Hogan 2308 13 2 Muni 0.5 0.0051657
James Patrick Kenney 2798 6 1 Muni 0.2 0.002139
Joseph Luebbers 4698 25 8 Muni 0.5 0.0052842
William Mallory 8277 38 9 Muni 0.5 0.0045552
Melba Marsh 8219 34 7 Muni 0.4 0.0041162
Beth Mattingly 2971 13 1 Muni 0.4 0.0043551
Albert Mestemaker 4975 28 9 Muni 0.6 0.0055865
Mark Painter 2239 7 3 Muni 0.3 0.0031124
Jack Rosen 7790 41 13 Muni 0.5 0.0052631
Mark Schweikert 5403 33 6 Muni 0.6 0.0060639
David Stockdale 5371 22 4 Muni 0.4 0.0040763
John A. West 2797 4 2 Muni 0.1 0.001427
Totals 182908 2368 320

Table 3: Probability of Appeal by Judge

Case based reasoning (CBR) as it is known in the field of economics, looks to classificatory solutions to ‘next term’ decisions within legal analysis. Graph formats assist in illustration of the logic model.  Others systems merely render a visual depiction that serves to assist the argument structure. The Araucaria system does not include in-depth fact pattern analysis of elements, but is intended to abstract those elements into a logical pattern or argument structure (Moens, 2007).

Figure 1

Araucaria system

Figure: 1 Araucaria system

Table 4

   Judge Disposed Appealed Reversed Court Prob RV (%) Prob RV (.)
Fred Cartolano 3037 137 12 Common 0.004 0.0037664
Thomas Crush 3372 119 10 Common 0.3 0.0028563
Patrick Dinkelacker 1258 44 8 Common 0.6 0.0061068
Timothy Hogan 1954 60 7 Common 0.3 0.0034636
Robert Kraft 3138 127 7 Common 0.2 0.0021393
William Mathews 2264 91 18 Common 0.8 0.0075853
William Morrissey 3032 121 22 Common 0.7 0.0069291
Norbert Nadel 2959 131 20 Common 0.9 0.0093247
Arthur Ney Jr. 3219 125 14 Common 0.4 0.0042839
Richard Niehaus 3353 137 16 Common 0.5 0.0045636
Thomas Nurre 3000 121 6 Common 0.2 0.0019187
John O’Connor 2969 129 12 Common 0.4 0.0038585
Robert Ruehlman 3205 145 18 Common 0.6 0.0057877
J. Howard Sundermann Jr. 955 60 10 Common 1.0 0.009756
Ann Marie Tracey 3141 127 13 Common 0.4 0.0039622
Ralph Winkler 3089 88 6 Common 0.2 0.001885
Penelope Cunningham 2729 7 1 Domestic 0.04 0.003653
Patrick Dinkelacker 6001 19 4 Domestic 0.07 0.000664
Deborah Gaines 8799 48 9 Domestic 0.1 0.0010162
Ronald Panioto 12970 32 3 Domestic 0.02 0.0002306
Mike Allen 6149 43 4 Muni 0.06 0.0006455
Nadine Allen 7812 34 6 Muni 0.08 0.0007641
Timothy Black 7954 41 6 Muni 0.07 ,0007499
David Davis 7736 43 5 Muni 0.06 0.00064223
Leslie Isaiah Gaines 5282 35 13 Muni 0.2 0.002439
Karla Grady 5253 6 0 Muni 0 0
Deidra Hair 2532 5 0 Muni 0 0
Dennis Helmick 7900 29 5 Muni 0.06 0.0006301
Timothy Hogan 2308 13 2 Muni 0.09 0.0008609
James Patrick Kenney 2798 6 1 Muni 0.04 0.0003565
Joseph Luebbers 4698 25 8 Muni 0.2 0.0016909
William Mallory 8277 38 9 Muni 0.1 0.0010788
Melba Marsh 8219 34 7 Muni 0.08 0.0008474
Beth Mattingly 2971 13 1 Muni 0.03 0.000335
Albert Mestemaker 4975 28 9 Muni 0.2 0.0017956
Mark Painter 2239 7 3 Muni 0.1 0.0013339
Jack Rosen 7790 41 13 Muni 0.2 0.0016573
Mark Schweikert 5403 33 6 Muni 0.05 0.0005512
David Stockdale 5371 22 4 Muni 0.07 0.0007411
John A. West 2797 4 2 Muni 0.07 0.0007135
Totals 182908 2368 320

 Table 4: Probability of Reversal by Judge

Within the courtroom, a dense quality of rhetorical argument posits the case. Rhetorical Structure Theory offers much in terms of common law cases as fact elements become intertwined with the formation of law itself (Mann and Thompson (1988). This is particularly the case in business related contract litigation, where contract language constitutes the locus of the decision. As seen in Figure 2, exploitation of the RST tree structure with the representation of clauses and sentences in the nodes, discourse relations at the edges computation of individual nodes is based on their position in the tree hierarchy (Marcu 2002).

Figure 2

Rhetorical Structure

Figure 2: Rhetorical Structure

Table 5

   Judge Disposed Appealed Reversed Court Ratio AP:RV
Fred Cartolano 3037 137 12 Common 8.8
Thomas Crush 3372 119 10 Common 8.4
Patrick Dinkelacker 1258 44 8 Common 18.0
Timothy Hogan 1954 60 7 Common 11.7
Robert Kraft 3138 127 7 Common 5.5
William Mathews 2264 91 18 Common 5.1
William Morrissey 3032 121 22 Common 18.2
Norbert Nadel 2959 131 20 Common 15.3
Arthur Ney Jr. 3219 125 14 Common 11.2
Richard Niehaus 3353 137 16 Common 11.7
Thomas Nurre 3000 121 6 Common 5.0
John O’Connor 2969 129 12 Common 9.3
Robert Ruehlman 3205 145 18 Common 12.4
J. Howard Sundermann Jr. 955 60 10 Common 5.0
Ann Marie Tracey 3141 127 13 Common 10.2
Ralph Winkler 3089 88 6 Common 6.8
Penelope Cunningham 2729 7 1 Domestic 14.3
Patrick Dinkelacker 6001 19 4 Domestic 21.0
Deborah Gaines 8799 48 9 Domestic 18.8
Ronald Panioto 12970 32 3 Domestic 9.9
Mike Allen 6149 43 4 Muni 9.3
Nadine Allen 7812 34 6 Muni 17.6
Timothy Black 7954 41 6 Muni 14.6
David Davis 7736 43 5 Muni 11.6
Leslie Isaiah Gaines 5282 35 13 Muni 37.1
Karla Grady 5253 6 0 Muni 0
Deidra Hair 2532 5 0 Muni 0
Dennis Helmick 7900 29 5 Muni 17.2
Timothy Hogan 2308 13 2 Muni 15.4
James Patrick Kenney 2798 6 1 Muni 16.7
Joseph Luebbers 4698 25 8 Muni 32.0
William Mallory 8277 38 9 Muni 23.7
Melba Marsh 8219 34 7 Muni 20.6
Beth Mattingly 2971 13 1 Muni 7.7
Albert Mestemaker 4975 28 9 Muni 32.1
Mark Painter 2239 7 3 Muni 42.9
Jack Rosen 7790 41 13 Muni 31.7
Mark Schweikert 5403 33 6 Muni 18.2
David Stockdale 5371 22 4 Muni 18.2
John A. West 2797 4 2 Muni 50.0
Totals 182908 2368 320

Table 5: Reversal given appeal by Judge

Descriptive ratio of reversals above indicates probability of a reversal by each judge. Interestingly, while a range of statistical significance can be drawn from the results there is not enough information to draw solid conclusions. In spite of the scientific method of rule application within court decision, the factoring of human intuition, and especially in the common law courtroom with jury decision as an aspect of procedure, each case is in actuality a 50/50 chance for reversal.

Following the Moens (2007) innovation of the SALOMON system, and subsequent study toward summarization of Belgian criminal cases, user application of the instrument led to information on ‘next term’ decisions. The test set was comprised of 1000 cases. Of those cases, 882 were general complaints, and 112 specific — the latter mostly appellate court procedures.  Recommendation to the findings resulted in the instrument illustrated in Figure 2.

Figure 3

SALOMON system

Figure 3: SALOMON system

Conclusion

The initial query to the study included the proposition that analysis of judicial appellate court decision might result in outcomes that could be understood in terms of job performance. Due to the limits to information on the origin of the count as it pertains to number of decisions, and in regard to the specific nature of the cases represented by ‘disposals,’ ‘appeals’ and resultant ‘reversals,’ little might be said about those numbers short of immediate statistical significance as it relates to the actual dataset. In short, number of cases certainly supports the argument that a particular justice has spent more significant time in the courts than colleagues, but ultimately it does little to depict professional performance, nor rationale behind decision to the appeals without case record.

References

De Mot, J. and Depoorter, B. (2010). Tort Law and Probabilistic Litigation: How to Apply Multipliers to Address the Problem of Negative Value Suits. International Review of Law and Economics, 4 (3), 2010. doi:10.1016/j.irle.2010.04.003

Hillel, M.B. (2009). Probabilistic analysis in legal factfinding.  Acta Psychologica, 56(1-3) August 1984,  267-284. doi:10.1016/0001-6918(84)90024-6

Mann, W.C. and Thompson, S.A. (1988). Rhetorical structure theory: Toward a functional theory of text organization. Text 8 (3) (1988), 243–281.

Marcu, D. (2000). The theory and practice of discourse parsing and summarization. Cambridge, MA: The MIT Press.

Moens, M.F. (2007). Summarizing court decisions. Information Processing & Management, 43 (6), November 2007, 1748-1764. doi:10.1016/j.ipm.2007.01.005

Saks, M.J and Schweitzer, N.J (2009). The Gatekeeper Effect: The Impact of Judges’ Admissibility Decisions on the Persuasiveness of Expert Testimony. Psychology, Public Policy, and Law, 15 ( 1), February 2009, 1-18.

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