Fraudulent Science Talks

Stanford University Research study Abstract

Today, a  GA reader  sent me a Standford Press article published on November 16, 2015, titled, "Stanford researchers uncover patterns in how scientists lie about their data," with this note:

Here's a timely article on Petro's bullshittery. 

From the article:

"... a pair of Stanford researchers have cracked the writing patterns of scientists who attempt to pass along falsified data.  ...Studies have shown that liars generally tend to express more negative emotion terms and use fewer first-person pronouns."

"Negative emotion terms" like "superficial rebranding" and "toxic and failed Kids First group"?

excerpt: Dr. Petrosino's crapademic masterpiece: "Hoboken Board of Education Results of November 2014 - A Deeper Look at the Numbers- Nov. 8, 2014

Does a  Pants-on-Fire Politifact Truth-o-meter count as a "negative emotion term"?

Dr. Petrosino's Education Project used such animated Truth-o-meters in his October 25, 2012 article "Assessing the Educational Claims of Kids First- Parsing Rhetoric and Hyperbole From Leadership and Reform."

Since when do PhD's use Pants-on-Fire Truth-o-Meters in their writings? How about "toxic and failed" former Board of Education employees seeking revenge on Board members who declined to renew their employment?

The Standford Press article continues:

"Fraudulent papers had about 60 more jargon-like words per paper compared to unretracted papers," Markowitz said. "This is a non-trivial amount.""

GA's term for "jargon-like  words" is "mumbo jumbo."

So, the researcher is saying that a fraudulent scientific paper has way more mumbo-jumbo than a genuine one; the Stanford study calls it "obfuscation."

The Petrosino Education Project's November 8, 2014 Petrosino's  "Hoboken Board of Education Results of November 2014 - A Deeper Look at the Numbers contains mumbo jumbo so thick, the reader may forget that Petrosino omits the "VBM" factor from his analysis, This is rather incredible, since 466 VBMs to one candidate was a decisive 'outlier.'

Here it comes... a blinding blizzard of mumbo-jumbo:

In statistics and probability theory, the standard deviation (SD) measures the amount of variation or dispersion from the average... A low standard deviation indicates the data points tend to be very close to the mean (or "expected value"); a high high standard deviation indicates that the data points are spread out over a large range of values. Table 3 shows that the "Parents for Progress" candidates experienced fairly low variability- meaning if a voter voted for one member of "Parents for Progress", they voted for all three. When voters deviated from voting for the entire ticket, evidence seems to indicate they voted for someone other than Antonio Gray (-87 votes from average, see Table 1). Table 3 also shows that the "Parents for Change" candidates experienced low to moderate variability- meaning if a voter voted for one member of "Parents for Change", they probably voted for all three. When voters deviated from voting for the entire ticket, evidence seems to indicate they voted for someone other than Lynn Danzker (-289 votes from average, see Table 1). Finally, Table 3 indicates that the "Education for All Children" candidates experienced moderate variability....  However the story seems to be a little more complicated with "Education for All Children." The fairly high variability likely came from Peter Biancamano (+243 votes from average, see Table 1 and Table 4) picking up votes from both "Parents for Change" and "Parents for Progress" voters . If a voter voted for one member of "Education for All Children", they likely voted for both but when an "Education for All" candidate received a vote from a non-"Education for All" voter, it likely came from a "Parents for Change" voter with some additional voters being "Parents for Progress" voters. Table 4- Click to enlarge Table 4 groups the candidates by ticket via a color coded bar graph. The standard deviation for the votes received by all 8 candidates was 253.5 and the average vote count for each candidate was 2097 (see Table 5). Further analysis indicates just how decisive Biancamano's victory was on Election Day. Table 6 is a table of candidates ranked by standard deviation units (calculated by subtracting the average vote total for the group from the individual candidate's vote total and then dividing by the standard deviation of votes for all candidates). In this election, a standard deviation unit was approximately 253 votes. Table 7 is a graphical representation of candidates color coded by slate on standard deviation units to help inform and model a normal distribution curve (Table 8).

Well, ain't that a triumph of obfuscation. Note, the tonnage of  mumbo-jumbo omits Biancamano's "decisive" 466 VBM non-standard deviation.

The Standford Press article continues:

"We believe the underlying idea behind obfuscation is to muddle the truth," said Markowitz, the lead author on the paper. "Scientists faking data know that they are committing a misconduct and do not want to get caught. Therefore, one strategy to evade this may be to obscure parts of the paper. We suggest that language can be one of many variables to differentiate between fraudulent and genuine science...."

Speaking of conduct...

Petrosino's PROSBUS & CURIOUS GAL POSTS

Bajardi v Pincus: MSJ Plumb. Cert; Exhibits KK,LL-prosbus, Curious Gal posts