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This paper by D’Ignazio and Klein emphasizes the importance of omission. Previously, we have discussed the dangers of omission in relation to Simpsons paradox and amounted variable bias (described by my classmate as occurring when “a variable that is correlated with both the dependent and one or more included independent variables is omitted from a regression equation.” In this case, unacknowledged confounding variables account for misleading conclusions. In a more recent article, omission is discussed in the context of digital humanities. As another classmate of mine said, “Lauren Klein brings up a very pertinent question on how scholars account for absences in the archival record. Interested in revealing the absence of information regarding Jefferson’s head Chef, James Hemings,” Klein developed a social network (as the author of the Paul Revere work also did to uncover hidden data) and uncovered a wealth of information concerning Hemings that had not otherwise surfaced.

Hemings was a slave, yet was one of the few who was literate. However, despite their friendship, even Jefferson himself refused to write Hemings directly. Thus, we must turn to the mentions of Hemings in Jefferson’s letters to others. This tactic, while a valuable source of information that would not otherwise have been readily available, is still plagued by the issue of omission and bias. Hemings was a slave, a member of a marginalized population of which few were literate. Women too, were often illiterate, and even those in such groups who could write were often not taken seriously and certainly not published (unless under an alias).

This is the issue that is addressed by D’Ignazio and Klein. Their feminist theory (which advocates not only for women but all marginalized groups), seeks “to challenge the idea that science and/or technology is objective and neutral by demonstrating how scientific thought is situated in particular cultural, historical, economic, and social systems. Feminist STS, both implicitly and explicitly, looks to the perspectives of those marginalized by current power configurations (including and especially those marginalized because of gender, sexuality, race, and/or ethnicity) as a way of exposing how their perspectives are not included in what is considered “objective”truth.”

A long quote, but one that accurately summarizes their mission, and introduces yet another reason for caution when analyzing data of the digital humanities. It is so easy to look at a data set or a work and to pick out what’s wrong, yet its harder and perhaps more important to think further and consider what’s missing. Similar to considering confounding variables that may result in omission bias and ruin the validity of the study, one must not accept the records of history as objective fact.