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The dissemination of divination in roman republican times

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A box-plot was made and revealed the presence <strong>of</strong> four outliers. <strong>The</strong> first was from 217,<br />

which was at the height <strong>of</strong> the 2 nd Punic war, probably the greatest war <strong>in</strong> late <strong>republican</strong><br />

memory. <strong>The</strong> only source for this is Livy. He some<strong>times</strong> uses prodigies to dramatize events<br />

<strong>in</strong> general (Levene 1993: 84-85). It is possible that he has here collapsed the prodigy lists for<br />

two years <strong>in</strong>to one (Levene 1993: 38). <strong>The</strong> second observation is from 44. <strong>The</strong> explanation<br />

here is analogous. 44 was the year where Caesar was killed. Here we have more sources, but<br />

they also seem to dramatize by list<strong>in</strong>g a greater number <strong>of</strong> prodigies, where some on closer<br />

<strong>in</strong>spection more str<strong>in</strong>gently could be called om<strong>in</strong>a. As we shall see <strong>in</strong> chapter 10, om<strong>in</strong>a were<br />

directed towards private persons and not the state as prodigia were. <strong>The</strong>re is therefore reason<br />

to doubt that most <strong>of</strong> the prodigies for the year 44 are prodigia <strong>in</strong> the technical sence, that is,<br />

<strong>in</strong> the same sense as the other prodigies <strong>in</strong> this <strong>in</strong>vestigation. <strong>The</strong> third observation stems<br />

from the year 104, where a serious attack <strong>of</strong> the Cimbri on North Italy took place at the same<br />

time as the second Sicilian slave war. It could therefore follow the pattern <strong>in</strong> Livy, where<br />

especially important events are dramatized with higher amount <strong>of</strong> prodigies by collaps<strong>in</strong>g<br />

two years and <strong>in</strong>tegrat<strong>in</strong>g count<strong>in</strong>g om<strong>in</strong>a as prodigies. Unfortunately we don’t have Livy’s<br />

history for this period, only the summary <strong>of</strong> Julius Obsequens. 102 <strong>The</strong>refore we cannot<br />

<strong>in</strong>vestigate this <strong>in</strong> detail. <strong>The</strong> last observation is from the year 163, where, as far as we can<br />

see, noth<strong>in</strong>g critical happened at all. Nevertheless 17 prodigies were reported. Unfortunately<br />

aga<strong>in</strong> we only have the prodigy lists from Julius Obsequens, so we cannot determ<strong>in</strong>e whether<br />

it is attributed to any dramatiz<strong>in</strong>g effect, but it can be seen that the prodigy lists from the<br />

previous year are miss<strong>in</strong>g. Consequently, it is probable, either by purpose or mistake, that the<br />

lists from 164 and 163 were collapsed <strong>in</strong>to one. <strong>The</strong>se four outliers were elim<strong>in</strong>ated from the<br />

data, s<strong>in</strong>ce they were likely to skew the trend <strong>in</strong> the data.<br />

<strong>The</strong> correlation coefficient for the trimmed data was 0,173 (r squared was 0,020), which is<br />

not a very good correlation. 103 A l<strong>in</strong>ear regression analysis was made with degree <strong>of</strong> crisis as<br />

the <strong>in</strong>dependent variable and number <strong>of</strong> prodigies as the dependent variable. This did not<br />

reveal any significant relationship between degree <strong>of</strong> crisis and number <strong>of</strong> prodigies F(1,102)<br />

= 3,151, P= 0.079. 104 Other regression models were tried, but the best fit was provided by the<br />

l<strong>in</strong>ear regression. <strong>The</strong>re is however a slight trend towards correlation.<br />

102 Julius Obsequens was an historian who extracted the prodigy lists and other portentuous events from Livy<br />

and made them <strong>in</strong>to a separate work (Schmidt 1968)<br />

103 For the untrimmed data r= 0.18 and r squared= 0.025.<br />

104 <strong>The</strong> result for the untrimmed data was F(1,106)= 3.730, p= 0.056. This is actually slightly better than the<br />

trimmed data, but still not significant.<br />

134

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