2. Literature Review
2.6 Defining Relevant Terms
Throughout the literature, there is much overlap between and confounding of the pairs of terms escalation and deescalation, conflict and cooperation, and war and peace. Based on the definition established by the Uppsala Conflict Data Program, in which an armed conflict involves “a minimum of 25 battle-related deaths per year and per incompatibility,” the South China Sea maritime territorial disputes have 94 only twice led to such an outcome. In the first instance, the military engagement between China’s PLAN and the South Vietnam Navy over control of the Crescent Group of the Paracel Islands on January 20, 1974, resulted in a reported 18 Chinese and 53 Vietnamese casualties. This clash is commonly referred to as the Battle of 95 the Paracel Islands. In the second instance, clashes between PLAN and Vietnam People’s Army (VPA) forces in and around Johnson South Reef in the Spratly Islands on March 14, 1988, led to a reported 64–74 Vietnamese casualties according to different sources.
The GDELT 1.0 Event Database uses the terms conflict and cooperation in its dataset, coding each record on a spectrum ranging from -10 (conflictive) to 10
(cooperative). Referred to as Goldstein values, they are based on the work of Joshua S.
Goldstein, whose weighted ranking of event types on a conflict–cooperation scale has become the accepted standard for many different databases of time-series event data. The GDELT 2.0 GKG uses the terms positive and negative tone in its dataset. 96 Theoretically, these values can range from -100 (negative tone) to 100 (positive tone) but typically fall between -10 and 10 in practice. In this dissertation, tensions are measured based on these data. These definitions are discussed in greater detail in {3.2 Data Selection}.
94 “Definition of Armed Conflict,” Uppsala Conflict Data Program, Uppsala: Department of Peace and Conflict Research, Uppsala University, 2014,
<http://www.pcr.uu.se/research/ucdp/definitions/definition_of_armed_conflict/>.
95 Bruce A. Elleman, “China’s 1974 Naval Expedition to the Paracel Islands,” in Bruce A. Elleman and S. C. M. Paine (eds.), Naval Power and Expeditionary Wars: Peripheral Campaigns and New Theatres of Naval Warfare , 141–151, Abingdon and New York: Routledge, 2011, p. 145.
96 Joshua S. Goldstein, “A Conflict-Cooperation Scale for WEIS Events Data,” Journal of Conflict Resolution 36(2): 369–385, June 1, 1992, pp. 376–377,
<https://dx.doi.org/10.1177/0022002792036002007>.
This dissertation makes use of the term tensions as well as the terms escalation and deescalation. Tensions is a preferable alternative to other terms
because it is less suggestive of a direction (as with “escalation” and “deescalation”) or specific incident (as with “conflict” and “cooperation”) and more accurately describes the ongoing nature of South China Sea relations and its tendency to fluctuate.
Consequently, it better lends itself to being represented on a scale. Escalation is defined as an increase in the level of tensions; deescalation, as a decrease. When in reference to the data, the terms conflict and cooperation are used in line with the GDELT interpretation borrowed from Goldstein. When the terms armed conflict and war are used not in reference to the data, statistical analyses, or GDELT, the UCDP definitions are used.
As for the “South China Sea” as a geographic area, there are several possible definitions of the term. One that is broadly accepted is for general purposes is the International Hydrographic Organization’s definition published in 1953, which gives specific geographic coordinates tracing the geographic boundary of the maritime area. Such a definition, however, is unsuitable for the purposes of this dissertation. 97 In the analyses using the GDELT 1.0 Event Database, the South China Sea is defined as inclusive of event records for which the geographic location of the action is the Paracel Islands or Spratly Islands. In the analyses using the GDELT 2.0 GKG, the South China Sea is defined as inclusive of event records for which the term “South China Sea” is identified as a mentioned geographic location. These definitions are discussed in greater detail in {3.2 Data Selection}.
97 Limits of Oceans and Seas (Special Publication No. 23), 3rd Edition , International Hydrographic Organization, 1953, pp. 30–31, <http://www.iho-ohi.net/iho_pubs/standard/S-23/S23_1953.pdf>.
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3. Methodology
This dissertation uses time-series event data from the Global Database of Events, Language, and Tone (GDELT), including both the GDELT 1.0 Event Database and the GDELT 2.0 Global Knowledge Graph (GKG). At the time of writing, this is the first GDELT-based analysis not only of South China Sea tensions but also of South China Sea issues in general. It is thus what is o en referred to as a ‘first cut’ at the data. There are three major inspirations for taking such a pioneering approach. First, the recognition that most research on the South China Sea disputes is based on limited, incomplete, or biased evidence or analytical frameworks makes it clear that there is a void needing to be filled with theoretically-grounded empirical research, particularly that of a quantitative nature. Second, acknowledgement of Lord Kelvin’s admonition stating that “if you can’t measure it, you don’t know what you are 98 talking about” suggests that the recently released GDELT event data offers 99 significant new opportunities for South China Sea research and international relations research more generally. Third, the decision to make use of two distinct statistical approaches derives from the essential diversity of evidence and research in international relations and the social sciences. As was observed over half a century ago, “In the present stage of development of the social sciences we cannot suggest one single approach as the method, and we must necessarily lean upon a fruitful eclecticism.” In short, the three inspirations for much of this dissertation’s 100 methodology include the need for new evidence-based approaches to South China
98 In its original form, the nineteenth-century Scottish physicist Sir William Thomson’s timeless exhortation reads, “I o en say that when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind: it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science , whatever the matter may be.” See William Thomson, Popular Lectures and Addresses , London and New York: Macmillan, 1889, pp. 73–74, <https://archive.org/details/popularlecturesa01kelvuo >.
99 Bernard M. Bass, “Two Decades of Research and Development in Transformational Leadership,”
European Journal of Work and Organizational Psychology 8(1): 9–32, 1999, p. 18,
<https://doi.org/10.1080/135943299398410>.
100 Lester G. Seligman, “The Study of Political Leadership,” American Political Science Review 44(4):
904–915, 1950, p. 914, <https://doi.org/10.2307/1951291>.
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Sea research, the recent availability of applicable data, and the importance of analyses inclusive of multiple statistical approaches.
In addition to GDELT, the validity of the data is confirmed using official documents and news reports from relevant time periods in {3.2.2 Linking GDELT 1.0 Event Database Data to Real World Events} and {3.2.5 Linking GDELT 2.0 GKG Data to Real World Events}. In particular, these secondary sources are used to ensure that South China Sea tensions, as described in the datasets, provides an accurate
reflection of actual events related to the maritime territorial disputes. In other words, manual analyses are conducted to confirm that, when the data indicate that tensions were relatively high in a given month, there were indeed events that suggest the quantitative data should reflect this. Likewise, analyses are conducted to confirm that low levels of tensions in the data are the result of actual increased cooperation or positive tone related to South China Sea issues.
Preliminary data querying and exporting are done using BigQuery on Google Cloud Platform. Subsequent data analyses are conducted using the statistical
programming language R, RStudio, and relevant packages. Further details are
provided in {3.2 Data Selection}, {3.3 Statistical Approach for RQ1}, and {3.4 Statistical Approach for RQ2}, and the complete code can be found in {Appendix I: GDELT Data Selection Code for Google BigQuery} and {Appendix II: GDELT Data Analysis Code for R}.