Vito D'Orazio
Biographic Data
| ID | 4549253 |
|---|---|
| NAME | Vito D'Orazio |
| GIVEN NAMES | Vito |
| FAMILY NAME | D'Orazio |
| SIGNATURE | ORAZIO V D |
| AFFILIATIONS | The University of Texas at Dallas |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 49 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 2 |
The Militarized Interstate Dispute Dataset
In their latest research note in our ongoing dialogue about the Militarized Interstate Dispute (MID) dataset, Gibler, Miller, and Little (GML) reiterate their concerns about a high error rate in the MID dataset and challenge our prior replication studies, which showed that GML's recommended changes to the dataset caused few substantive changes in the core findings of two published studies. In this rejoinder, we offer further explanation of our de…
Updating the Militarized Interstate Dispute Data
In a recent article, Gibler, Miller, and Little (2016) (GML) conduct an extensive review of the Militarized Interstate Dispute (MID) data between the years 1816 and 2001, highlighting possible inaccuracies and recommending a substantial number of changes to the data. They contend that, in several instances, analyses with their revised data lead to substantively different inferences. Here, we review GML's MID drop and merge recommendations and ree…
Separating the Wheat from the Chaff
Due in large part to the proliferation of digitized text, much of it available for little or no cost from the Internet, political science research has experienced a substantial increase in the number of data sets and large-nresearch initiatives. As the ability to collect detailed information on events of interest expands, so does the need to efficiently sort through the volumes of available information. Automated document classification presents …
Updating the Militarized Interstate Dispute Data
In a recent article, Gibler, Miller, and Little (2016) (GML) conduct an extensive review of the Militarized Interstate Dispute (MID) data between the years 1816 and 2001, highlighting possible inaccuracies and recommending a substantial number of changes to the data. They contend that, in several instances, analyses with their revised data lead to substantively different inferences. Here, we review GML's MID drop and merge recommendations and ree…
Separating the Wheat from the Chaff
Due in large part to the proliferation of digitized text, much of it available for little or no cost from the Internet, political science research has experienced a substantial increase in the number of data sets and large-nresearch initiatives. As the ability to collect detailed information on events of interest expands, so does the need to efficiently sort through the volumes of available information. Automated document classification presents …
The Militarized Interstate Dispute Dataset
In their latest research note in our ongoing dialogue about the Militarized Interstate Dispute (MID) dataset, Gibler, Miller, and Little (GML) reiterate their concerns about a high error rate in the MID dataset and challenge our prior replication studies, which showed that GML's recommended changes to the dataset caused few substantive changes in the core findings of two published studies. In this rejoinder, we offer further explanation of our de…
Separating the Wheat from the Chaff
Due in large part to the proliferation of digitized text, much of it available for little or no cost from the Internet, political science research has experienced a substantial increase in the number of data sets and large-nresearch initiatives. As the ability to collect detailed information on events of interest expands, so does the need to efficiently sort through the volumes of available information. Automated document classification presents …
Updating the Militarized Interstate Dispute Data
In a recent article, Gibler, Miller, and Little (2016) (GML) conduct an extensive review of the Militarized Interstate Dispute (MID) data between the years 1816 and 2001, highlighting possible inaccuracies and recommending a substantial number of changes to the data. They contend that, in several instances, analyses with their revised data lead to substantively different inferences. Here, we review GML's MID drop and merge recommendations and ree…
The Militarized Interstate Dispute Dataset
In their latest research note in our ongoing dialogue about the Militarized Interstate Dispute (MID) dataset, Gibler, Miller, and Little (GML) reiterate their concerns about a high error rate in the MID dataset and challenge our prior replication studies, which showed that GML's recommended changes to the dataset caused few substantive changes in the core findings of two published studies. In this rejoinder, we offer further explanation of our de…
Artificial Intelligence (3 works) · Computer Science (3 works) · Artificial Intelligence (2 works) · Biology (2 works) · Economics (2 works) · Information retrieval (2 works) · Law (2 works) · Law (2 works) · Law and economics (2 works) · Miller (2 works)