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Academic Databases: Beyond Digital Literacy

Basic digital literacy for scholarly research includes knowing how to access digital archives, search them, and interpret their results.

Another component of digital literacy is familiarity with the semiotics of the interface; knowing how to “read” the instructions and symbols that give the user an idea of what invisible material lives in a database. These portals make the contents accessible, and also convey, before a search is even conducted, a range of search possibilities. The interface suggests something about the most useful metadata that the archive contains and the way the data can be accessed.

A user, then, can glean understanding about the mechanics of the database through the interface alone. This additional level of digital literacy is helpful, but still represents a limited understanding of databases. Many of the commonly used archives that humanities scholars, librarians, and historians use are proprietary, and even with some information and educated guesses about these archives’ metadata structures, it’s difficult or impossible to go a step deeper and discern exactly how the search algorithms work and how the database is designed.

This is an issue of emerging importance for digital scholars, and is prompting historians and others to think about what appears in search results and what doesn’t. But even if researchers knew how every database and its search algorithms worked, that wouldn’t resolve all the issues and theoretical implications of digital research and scholarship. As Ben Schmidt has pointed out, “database design constrains the ways historians can use digital sources.”

The limits of database design are an important window into the computational disciplines that enable information science in the first place. Programming machines to search a hybrid of digitized source materials is of course a wide problem, involving a myriad of methods, employing methods that are constantly evolving and becoming more powerful. Therefore, it’s interesting to ask: When are the issues associated with digital research contingent on computational science and when are they contingent on the way that proprietary archives and databases choose to implement the latest algorithms?

An interesting consideration in addressing this question might start with a distinction that William J. Turkel makes between scholars who use subscription archives and those who write code to mine massive data sets themselves. The literary scholar Ted Underwood has also discussed searching academic databases and data mining in parallel, commenting, “I suspect that many humanists who think they don’t need “big data” approaches are actually using those approaches every day when they run Google searches . . . Search is already a form of data mining. It’s just not a very rigorous form: it’s guaranteed only to produce confirmation of the theses you bring to it.”

Thinking about the distinction between proprietary database engineer and dataset hackers might foster the assumption that those two parties have radically different agendas or methods for searching born-digital and and digitized archive material. But while independent programmers represents a new frontier of sorts—scholars willing to learn the methods needed to do their own research and retrieve information from their own source material—they aren’t necessarily confronted by any fewer database design limitations than the engineers who work at Gale. This gets at the heart of what’s at stake for researchers in a digital age, and why this is an apt time to explore the way digital archives work, on a computational level.

Many automated, machine-driven search techniques are a set of instructions that don’t always produce predictable results, and can be difficult to reverse engineer even when bugs are discovered. Corporate engineers don’t have full control over the results they get, and neither do hackers or the authors of open-source software.

Why is that important? One goal of Beyond Citation is to explore and provide information on how databases work, so that scholars can better understand their research results. One could argue that scholars require so-called “neutral” technology; systems that don’t favor any one type or set of results over another. And it’s easier to understand and confirm search neutrality if algorithms and source code are publicly available. But exactly what is such neutrality, and would we know it if we saw it? Any algorithm, secret or otherwise, is a product of disciplinary constraints and intersections, and reveals the boundaries of what’s computationally possible. In short, the “correctness” of any algorithm is hard to nail down.

When we look more closely at the concept of neutrality, we see that both the user and the engineer are implicated in algorithmic design choices. James Grimmelman, a lawyer, has made a compelling argument that, “Search is inherently subjective: it always involves guessing the diverse and unknown intentions of users.” Code that’s written as a service to users is written with an interaction already in mind. Evaluating the nuances of search algorithms and determining the impact they make on the integrity of one’s research involves acknowledging these kinds of imagined dialogues.

These are just some exploratory thoughts, as none of these questions about database design and search can be taken in isolation. Beyond Citation, then, is a starting point for going beyond digital literacy in multiple directions. We are gathering and presenting the kinds of knowledge that might allow scholars to distinguish between computational limitations, the limits of metadata and the ways it’s structured, and the agendas of a proprietary company. As the project evolves, we ourselves hope to deepen the kinds of skills and knowledge that allow us to present such information in the most meaningful and usable ways.