This post is part of Antenna’s Digital Tools series, about the technologies and methods that media scholars use to make their research, writing, and teaching more powerful.

2,125… or was that 2,215? When working on my dissertation, a question that came up again and again when I said I was trying to look at entire series of several television shows was “how many episodes did you look at total?” It was a perfectly reasonable question, and yet one I often wasn’t quite sure of the exact number when I was asked it. After a certain point what was another 50 episodes or so? If I couldn’t easily remember the number of episodes I was looking at, I knew remembering the details of each one wasn’t going to be possible. As a result, finding a way to code and take notes on the shows I was examining, and make them searchable later, was one of the first steps I took during my dissertation process. Four years later, as the research approach I developed in my dissertation has become increasingly important to my work, I am still in search of the perfect software.

When I began looking for a solution for my dissertation, I ran into three problems that I suspect are pretty common: 1) I had never worked on a project that size and was not aware that there were software solutions out there; 2) the software I had heard of could cost several hundred dollars; and 3) (most importantly) I wasn’t sure exactly what I was looking for. My dissertation began largely from an interest in finding a different way to approach television texts and wanting to investigate how the form of different television genres and a number of different themes of representation intersected. As a result when I sat down with my first stack of teen drama DVDs to code I didn’t know quite what I wanted to code. It was through the process of coding, thinking about what information I would need and want to be able to go back to, that I learned what I was looking for. I only realized that something like acts of physical affection were something I wanted to code with a simply y/n and character names after 6 episodes. It turned out that a shorthand for demographic information (e.g. WASM for White Adult Straight Man) would be important for medical dramas and crime dramas to denote the demographics of criminals, victims, suspects, and patients, although it had been entirely unnecessary for teen dramas. Coding, for me, was a learning process — something that both recorded information, made it accessible, and helped me discover what I was looking for. That process of discovery through research certainly won’t be foreign to most academics. After all, there is joy in finding that unexpected piece of the puzzle in an archive or watching a focus group coalesce in an unexpected way. However, as I have found half-a-dozen or more software demos later, that is not quite how most academic research software works. Most of the software I experimented with wanted me to know what I was looking for, or at least already have what I was looking at (i.e. interview transcripts, survey results) in a concrete way.

Because of this core issue — the fact that how much information, what information, and what kind was constantly evolving — I found then, and again three years later, that it was an enterprise (read: business) not academic software that best suited my needs. During my dissertation it turned out to be the relatively straightforward Numbers spreadsheet software that did the job. For each genre, I would set up a different spreadsheet with the unique sets of information I needed for that genre. For example, for crime dramas there would be a column for each of the following: demographics of victims(s), demographic(s) of perpetrators, demographics of suspect(s), motive, outcome, religion, non-heteronormative sexuality, gender themes, police behavior, and the nebulous “notes” section that inspired the columns and code short-hands that I needed as things evolved.

What made Numbers work was that I was transparently typing in words, the shorthand I evolved to stand in for the boxes on a traditional “coding” sheet, and numbers (episode numbers, number of patients, etc.). I could always change what I coded and how. Every few episodes I watched I would ask, ‘Is there something important and new I want to track?’ If there was I could word search my notes and assign them shorthands; so, as time went on, I needed less notes and could shorthand much more of my fiftieth ER episode’s notes then I could when I began my fifth. The spreadsheets seemed disorderly and overwhelming to my partner when he peeked at my work (see image below) but they had the advantage of elasticity, changing as I learned what I was doing and what I was looking for.

Numbers didn’t have any assumptions (like a lot of more powerful software does) about what information I would be inputting and how I would use it. Therefore, when it came time to sort that information it also leant itself well to finding the relevant episodes and connections. The filter function allowed me to pick any column and any search term and would show me only the rows (episodes) that were relevant. Every episode that contains the word “jealousy” in the motive column but not the words “anger” or “angry” and the religion code “CH” (for Christian) was only a few filter clicks away.

Like Elana Levine, I found that the software that was available couldn’t do the whole job itself. Numbers didn’t really recognize the information I was putting in as something it should count, so if I wanted to know how many white male victims of crimes there were (hint: a lot) I was on my own to physically count them up. As a result I discovered that Zotero, a research material collection system (similar to Scrivener) that I had been using for reading notes and collecting PDFs also helped me analyze those thousands of episodes. After filtering the information using Numbers, I would create files in Zotero where I would list all the episode numbers that discussed Buddhism, or in which a lesbian character appeared, or in which a patient died. I’d then count up the numbers of episodes in a given category. Because Zotero was so searchable, it made it quick and easy to find all the “important themes” a given episode dealt with and calculate all kinds of relationships that I hadn’t originally expected to look at (percentage of patient deaths that were pregnant women? Alcoholics? Coming right up!).

Spreadsheets and a digital version of a filing cabinet (my best way of describing Zotero) are not necessarily the high-tech solutions I might have initially sought but their content agnosticism and searchability made them perfect fits for the work I was doing at the time. Just the other day I pulled up one of my old spreadsheets looking for the sort of thing I hadn’t coded but likely would have kept in the episode notes, and found an episode of a medical drama featuring an elementary school teacher in mere moments. When I started my new job and embarked on new research projects, including those that required collaboration, I started to feel like spreadsheets just wouldn’t do the job anymore and went in search of the perfect software. One year, several meetings with my college’s IT guys, and quite a few demo downloads later and I still haven’t found it. My new, better spreadsheet alternative has turned out to be yet another business solution: FileMaker Pro. And the shoe still doesn’t quite fit, but more on that later (stay tuned for Part II next week).

While I might not have discovered the perfect piece of software, what I have discovered is that the creative use of open-ended software can serve the study of texts well. However, the available research software is not yet designed for the diversity of information, multiplicity of data input types, and unique twists and turns that accompanies the study of media texts.

Post by Elana Levine, University of Wisconsin-Milwaukee

This is the second in a series of posts detailing my use of digital tools in a television history project. Read Part I here.

When I set out to manage all of the research materials for my history of US daytime television soap opera digitally, I was mainly concerned with having a system for storing PDFs, notes, and website clippings in a way that made them easily searchable. But after I had decided to use DevonThink as my data management system, migrated existing materials into the program, and began taking new notes with the software, I had to face the second part of my process—converting research materials into chapter outlines.

As I described in my previous post, my earlier method for this stage involved a floor and piles of papers. It also involved blank notecards, on which I would write labels or topics for the different piles as I sorted them into categories, and then a legal pad and pen, upon which I would sketch an outline of my chapter, figuring out the connections across the piles/categories, and testing ideas for the big picture arguments to which the piles built. Having gone digital, however, there were no physical piles of paper to organize. I needed a digital means of conducting that analog process. I needed digital piles.

For a while, I was resistant to considering writing software as the answer to this dilemma. Writing was not the problem. I had been writing digitally for a long time. (No, you need not remind me of the typewriter I took to my freshman year of college). Because I did so much planning and thinking before writing, I had no problem using conventional word-processing software to write. In fact, I like to write in linear fashion; it helps me construct a tight argument and narrate a coherent story. It was the outlining—the pile making, the planning and thinking—that I had to find a way to digitize. Then I saw the corkboard view on Scrivener with those lined 3X5 index card-like graphics. A virtualization of my piles, beaming at me from the screen instead of surrounding me on the floor!

The “Binder” feature in Scrivener.

The “Corkboard” view in Scrivener.

So began my experimentation with Scrivener, which has now become an integral part of my process. Scrivener is writing software and, like DevonThink or any other digital tool, has many uses. As with my use of DevonThink, I have been learning it as I go, so I am far from expert in all of its features. Because I needed the software to help me to categorize my research materials and outline my chapters, I mainly use its “binder” feature to sort my materials into digital piles. The hierarchical structuring of folders and documents within the Scrivener binder provides me with a way of replicating my mental and, formerly, haptic labor of sorting and articulating ideas and information together in a digital space.

I began by reading through all of the materials in DevonThink associated with the 1950s. As I read I categorized, figuring out what larger point the source spoke to, or what circumstance it served as evidence of. I created what Scrivener calls “documents” for any piece of research, or connected pieces of research, that I thought might be useful in my chapters. Early on, I realized I had multiple chapters to write about the ‘50s and ended up outlining three chapters at once as I moved through my materials. I gradually began to group documents into folders labeled with particular themes or points. This is the equivalent to me putting an index card with a label or category on top of a pile of papers, a way of understanding a set of specific pieces of information as contributing to a larger point or idea. These folders became sub-folders of the larger chapter folders. But it is the way I integrate this process with DevonThink that allows me to connect specific pieces of my archive to my argument. In DevonThink I am able to generate links to particular items in the database. I paste those links in the Scrivener documents I create.

How does this look in Scrivener? Sometimes this means that a Scrivener document is just my link, the text of which is the name of my DevonThink item, such as, “SfT timeline late ‘50s/early ‘60s,” which is my notes on story events from Search for Tomorrow during that period. But Scrivener’s “Inspector” window, which can appear alongside the document on the screen, is a useful space for me to jot down notes about that document, reminding myself of the information it offers or indicating what I see as most relevant about it. The synopsis I create here is what I see if I look at my documents in the corkboard view.

The “Portia and Walter relationship” document in Scrivener.

Other times my Scrivener documents include a number of DevonThink links that feed into the same point. For example, a document called “Portia and Walter relationship” includes links to five different items in DevonThink, four of which are notes on Portia Faces Life scripts; the fifth is notes on memos from the show’s ad agency producer to writer Mona Kent. In my synopsis notes on this document, I reminded myself that these were examples of the ways that married couple Portia and Walter talked to each other as equals, and how this served as a contrast to another couple on the show, Kathy and Bill. This ability to link to my DevonThink archive has allowed Scrivener to serve as my categorizing and outlining system.

While I have written sentences here and there in Scrivener to help me remember the ideas I had about particular materials, I have not yet found need to actually write chapters within it—I use a conventional word processing program for that. I know this is unlike the typical use of the software, but working this way has helped me to manage an otherwise unwieldy task. Scrivener provides a way to include research materials within its structure, but does not have the functionality for managing those materials that I get with DevonThink.

The “free form text editor” Scapple.

This system is working well for me, but at times I do find the Scrivener binder structure to be too linear. The ability to move my paper piles around, to stack them or spread them apart, was a helpful feature of my analog methods. As a result, I have begun experimenting with Scapple, a “free form text editor,” similar to mind-mapping software and created by Scrivener’s publishers, as a way to digitally reimagine the fluidity of the paper piles. Like Scrivener, Scapple allows me to link to DevonThink items and has met my desire for a non-linear planning system. I can connect examples and items from my archive to larger points and, through arrows and other forms of connection, note the relationship of particular pieces of data to multiple concepts.  I’m not yet convinced it is essential to my workflow, but I am intrigued by its possibilities and eager to keep experimenting within the generous trial window (which Scrivener and DevonThink both have, as well).

My use of these digital tools is surely quite idiosyncratic, but in ways more specific to me than to my object of study or the field of television historiography. More particular to the history of soaps and to media history in general are the challenges of managing video sources. Tune in next time for that part of my story.