More evidence of fraud in the recall vote

October 3, 2006

In four earlier posts, I presented a description of the work of Delfino and Salas and the complementary work of Medina on the evidence for fraud in the recal referendum. I wrote four posts on the subject, which you can find here, here, here and here. In the second one of those posts I discussed the parameter “k” a measure of the proportion of fraction of Si or Yes votes to recall Hugo Chavez, divided by the number of people in the same center that signed the petition to recall Hugo Chavez:

      Yes(Si) Votes
k= ——————


As a function of another “normalized” parameter s

s= ——————
     Total Votes

In the latest version of the paper, now in English, Delfino and Salas have added a compelling graph of k seen here:

Fig. 1. k as a function of the total number of votes on equal scales at each center for manual (left) and automated (right) centers. (Open circles are centers abroad)

What this plot does is to show k as a function of the number of total votes for voting centers of equal sizes, so that no distortions are introduced by the absolute number of voters. What can be seen is that the manual centers show a lot of fluctuations or scatterat smaller centers, which is what you expect. as the number of voters becomes small. This is because k in some sense measures how good a predictor the signatures were of the actual Si vote to recall Chavez, but as centers become smaller, the accuracy will diminish because the statitistics are “worse” since the number of voters is smaller. That is why you see scatter for small number of votes on the manual centers.

The problem is, that since the size of these centers are the same, one should see the same whether the centers are automated or not. But this simply does not happen as seen on the plot on the right for automated centers. In fact, for smaller centers the automated case curiousl seems to show even less fluctuations, which is absolutely counterintuitive. This is further evidence that the automated vote was manipulated at the recall referendum.

Some people have argued that the problem is that manual centers tend to occurr in more rural or sparsely populated areas, so that the data above simply reflect  socioeconomic or socio cultural differences between the manual and automated centers.

What Delfino and Salas did the, is to select centers which are classified by the CNE as “Mixed Townships” and “hamlets” and plot the data for these two specifica cases separately. This is shown below in Fig. 2:

Fig. 2. k as a function of s (defined above) for manual centers (left) and automated centers (right) in “Mixed Townships” (top) and “hamlets” (bottom)

As can be seen, the “strange” absence of scatter or fluctuations, still occurs in the automated centers when these two types of population centers are considered, while the manual centers in both cases show the expected scatter or fluctuations. This is once again evidence that the automated results were somehow manipulated and the data from the recall petition was somehow used mathematically to generate the results, rather than the actual vote.

If you are not mathematically inclined, Delfino and Salas have posted a presentation called “The ABC of the Referendum” (In Spanish, soon in English), where they try to make it simple to understand. You can think of this as k being how good a predictor of the vote in the recall was. If in the automated center k is of the order of one, this means there were as many votes to recall as signatures in the petition to have the recall take place. As k increases, it means there were more and more votes than you would have thought just from the signatures.

Below is a Google maps image taken from the Delfno and Salas presentation. It represents a Parish of the municipality of Valencia, thus, nearby centers are similar in socioeconomic profile. But as you can see, while the automated centers (blue) have k near 1, the manual centers (green) have k’s as large as 4.3 in one case, despite the fact that that particular center is very close to automated centers where k barely moved above 1.This makes abslutely no sense unless the data was faked.

Could it be clearer than that?

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