gml_mid_ddydisps: Directed Dyadic Dispute-Year Data with No Duplicate...
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gml_mid_ddydisps R Documentation
Directed Dyadic Dispute-Year Data with No Duplicate Dyad-Years (GML, v. 2.2.1)
Description
These are directed dyadic dispute year data derived from the Gibler-Miller-Little (GML) Militarized Interstate
Dispute (MID) project. Data are from version 2.2.1. These were whittled to where there is no duplicate dyad-years.
Its primary aim here is merging into a dyad-year data frame.
Usage
gml_mid_ddydisps
Format
A data frame with 9262 observations on the following 25 variables.
dispnuma numeric vector for the dispute number
ccode1a numeric vector for the focal state in the dyad
ccode2a numeric vector for the target state in the dyad
yeara numeric vector for the dispute-year
gmlmidongoinga numeric vector for whether there was a dispute ongoing in that year
gmlmidonseta numeric vector for whether it was the onset of a new dispute (or new participant-entry into a recurring dispute)
sidea1is ccode1 on side A of the dispute?
sidea2is ccode2 on side A of the dispute?
fatality1a numeric vector for the overall fatality level of ccode1 in the dispute
fatality2a numeric vector for the overall fatality level of ccode2 in the dispute
fatalpre1a numeric vector for the known fatalities (with precision) for ccode1 in the dispute
fatalpre2a numeric vector for the known fatalities (with precision) for ccode2 in the dispute
hiact1a numeric vector for the highest action of ccode1 in the dispute
hiact2a numeric vector for the highest action of ccode2 in the dispute
hostlev1a numeric vector for the hostility level of ccode1 in the dispute
hostlev2a numeric vector for the hostility level of ccode2 in the dispute
orig1is ccode1 an originator of the dispute?
orig2is ccode2 an originator of the dispute?
fatalitya numeric vector for the fatality level of the dispute
hostleva numeric vector for the hostility level of the MID
mindura numeric vector for the minimum duration of the MID
maxdura numeric vector for the maximum duration of the MID
recipa numeric vector for whether a MID was reciprocated
stmona numeric vector for the start month of the MID
Details
The process of creating these is described at one of the references below. Importantly, these data are somewhat
"naive." That is: they won't tell you, for example, that Brazil and Japan never directly fought each other during World War II.
Instead, it will tell you that there were two years of overlap for the two on different sides of the conflict and that the highest
action for both was a war. The data are thus similar to what the EUGene program would create for users back in the day. Use these
data with that limitation in mind.
References
Miller, Steven V. 2021. "How to (Meticulously) Convert Participant-Level Dispute Data to Dyadic Dispute-Year Data in R."
URL: http://svmiller.com/blog/2021/05/convert-cow-mid-data-to-dispute-year/
Gibler, Douglas M., Steven V. Miller, and Erin K. Little. 2016. “An Analysis of the Militarized
Interstate Dispute (MID) Dataset, 1816-2001.” International Studies Quarterly 60(4): 719-730.
peacesciencer documentation built on March 31, 2023, 8:37 p.m.
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| gml_mid_ddydisps | R Documentation |
Directed Dyadic Dispute-Year Data with No Duplicate Dyad-Years (GML, v. 2.2.1)
Description
These are directed dyadic dispute year data derived from the Gibler-Miller-Little (GML) Militarized Interstate Dispute (MID) project. Data are from version 2.2.1. These were whittled to where there is no duplicate dyad-years. Its primary aim here is merging into a dyad-year data frame.
Usage
gml_mid_ddydisps
Format
A data frame with 9262 observations on the following 25 variables.
dispnuma numeric vector for the dispute number
ccode1a numeric vector for the focal state in the dyad
ccode2a numeric vector for the target state in the dyad
yeara numeric vector for the dispute-year
gmlmidongoinga numeric vector for whether there was a dispute ongoing in that year
gmlmidonseta numeric vector for whether it was the onset of a new dispute (or new participant-entry into a recurring dispute)
sidea1is
ccode1on side A of the dispute?sidea2is
ccode2on side A of the dispute?fatality1a numeric vector for the overall fatality level of
ccode1in the disputefatality2a numeric vector for the overall fatality level of
ccode2in the disputefatalpre1a numeric vector for the known fatalities (with precision) for
ccode1in the disputefatalpre2a numeric vector for the known fatalities (with precision) for
ccode2in the disputehiact1a numeric vector for the highest action of
ccode1in the disputehiact2a numeric vector for the highest action of
ccode2in the disputehostlev1a numeric vector for the hostility level of
ccode1in the disputehostlev2a numeric vector for the hostility level of
ccode2in the disputeorig1is
ccode1an originator of the dispute?orig2is
ccode2an originator of the dispute?fatalitya numeric vector for the fatality level of the dispute
hostleva numeric vector for the hostility level of the MID
mindura numeric vector for the minimum duration of the MID
maxdura numeric vector for the maximum duration of the MID
recipa numeric vector for whether a MID was reciprocated
stmona numeric vector for the start month of the MID
Details
The process of creating these is described at one of the references below. Importantly, these data are somewhat
"naive." That is: they won't tell you, for example, that Brazil and Japan never directly fought each other during World War II.
Instead, it will tell you that there were two years of overlap for the two on different sides of the conflict and that the highest
action for both was a war. The data are thus similar to what the EUGene program would create for users back in the day. Use these
data with that limitation in mind.
References
Miller, Steven V. 2021. "How to (Meticulously) Convert Participant-Level Dispute Data to Dyadic Dispute-Year Data in R." URL: http://svmiller.com/blog/2021/05/convert-cow-mid-data-to-dispute-year/
Gibler, Douglas M., Steven V. Miller, and Erin K. Little. 2016. “An Analysis of the Militarized Interstate Dispute (MID) Dataset, 1816-2001.” International Studies Quarterly 60(4): 719-730.
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