Why has this research been accepted by independent scholars?
Research studies investigating an idea like peace-creating groups—a concept that transcends the dominant materialist paradigm of modern science—must run a gauntlet of highly skeptical scholars.
For those unfamiliar with research procedures, the publication of a scientific study implies more than mere printing and distribution. Most research journals are refereed—distinguished experts in the field (referees, or peer reviewers) judge every submitted study. When a theory is new or particularly controversial, referees assess the studies more strictly. Acceptance for publication indicates that the studies have been judged of professional quality, worthy of attention by the wider community of academic experts.
By now peace-creating assemblies have been thoroughly field-tested in more than 50 demonstration projects, and most of these demonstrations have been covered in 23 academically published scientific studies. Here are some of the reasons why research on such an innovative topic has attained such frequent acceptance:
Repeated findings: The more frequently a theory has been tested, the more confidence scientists place in the results. Nearly all of the published research on peace-creating assemblies reports on either a number of separate assemblies in one study (replication), or one assembly that increases and decreases in attendance many times (de facto replication).
Strong correlations: Scientists accept that cigarettes cause cancer because repeated studies show that the correlation between smoking and cancer is strong. In the same way, studies on peace-creating assemblies have been accepted for publication because they show strong correlation between peace-creating groups and reduced crime, warfare and terrorism.
Lead-lag analysis: In studies where attendance at the group of peace-creating experts varies over time, it is often possible to directly assess causation through lead-lag (or transfer function) analysis. Such analysis shows which changes first: peace-creating attendance or social violence (crime, war, etc.). In all the studies in which such lead-lag analysis has been possible, the evidence shows that the meditation attendance changes first, and the measures of social violence soon after. This strongly indicates a causal role for peace-creating groups, and has been decisive in the publication of key studies.
Ruling out alternate explanations: To be convincing, any study must rule out alternate possible explanations for the results. Since violent crime, for instance, typically increases as daily temperature increases, researchers must account for temperature changes in their analysis. In these studies, researchers have carefully demonstrated that alternate possible explanations—such as weather, regular weekly, monthly or seasonal changes, changes in police patrolling, etc.—cannot account for the evidence.
Studies easily replicable (use of open public data): One of the most convincing aspects of this research is the open, public nature of the evidence. Much sociological research is based on privately gathered data that other scientists can only accept on faith. The research on peace-creating groups, on the other hand, is doubly public:
- The dates and approximate attendance of most peace-creating assemblies are available in contemporaneous newspaper accounts.
- Statistics on social violence, including crime, accidents, warfare, terrorism, etc., are available to any researcher with access to public records.
This public nature of the evidence means that any given study is replicable by other researchers—a strong safeguard for scientific accuracy.
Time series analysis: Time series analysis helps researchers bring clarity to the bewildering complexity of social situations. This mathematical tool allows them to look at the recent history of crime, for instance, and construct a mathematical model that accounts for all the systematic cycles and trends in the data. In such a model, the functionally infinite number of influences in a society, most of them small, cancel one another out, and the model reflects only those significant influences (viz. weekends, weather) capable of causing measurable changes in crime. Moreover, if crime moves up and down in a monthly cycle, but the reason for this is not known, that monthly cycle is still reflected in the data and the time series model. The model can thus generate predictions about the immediate future of crime—if only the current causative factors (both known and unknown) are in play. If crime suddenly drops much lower than the prediction, therefore, it is not convincing to argue prior causes, since the evidence indicates a significant new causative influence.
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