Paige Morgan

** These are positions that were advertised externally, though we’re hoping to assemble another segment of the corpus that will focus on job descriptions that were created and hired internally, i.e., when an English subject liaison librarian position is changed into a DH librarian position.

There was a point in my digital humanities learning development when I knew that I wanted a DH job, and was trying to sort out what I could do to make getting one more likely. I wasn’t sure whether I could compete with students coming from more established DH programs, working closely with leading DH scholars — or at least, I couldn’t somehow magically give myself quite the same sort of training I was seeing elsewhere. I did think about what it would take to really go toe-to-toe, and I thought I might somehow manage it — but I also thought it might make me exhausted and miserable and ill; and I was cynical enough to doubt whether search committees would look twice at me anyways. Becoming knowledgeable about infrastructure was my workaround; a wager that studying the conditions, questions, desires, strategies, and anxieties related to DH program development and infrastructure at the University of Washington would help me be successful on the job market. It was a gamble. More than once, people asked me whether I wouldn’t be better off if I just focused on finishing the dissertation. More than once, I worried about whether I would be able to secure funding for another full year, funding that would allow me to keep working on the diss, but also allow me to do what I thought of as a self-designed internship in institutions and infrastructure.

What I discovered was that working with infrastructure was exactly what I wanted to do: looking at systems and how people reacted to and moved through them, adjusting the systems so that they would work better for the people involved, building new parts, or transforming existing parts into something different. I still struggle — I’m still learning — to articulate what it feels like to work with infrastructure, and why I like it so much. I like that there are constraints (which vary with context); I like that there are people, whose emotional energy is affected by the constraints (and can also shape constraints). I like that there are additional variables (some historical, others wild cards); and all of these have to be balanced and played against each other. To me, working with infrastructure feels very much like working with music (which I did intensely as a child and teenager).

Still, a year ago I couldn’t have written that last paragraph, because I was still growing into my chosen skill set. My CLIR postdoc was my sixth year of doing digital humanities, but it was the first time I’d gotten to do DH full-time, rather than on top of dissertating and teaching, and it was still a test of what officially working with infrastructure (and infrastructure challenges) would feel like. One moment of the fellowship stands out in my memory.  In December, I was preparing to team-teach an undergraduate DH course with my colleagues from the Sherman Centre, and I was worried that some aspects of it were under-planned — but I seemed to be the only one who was concerned, so I didn’t push it. As it turns out, I should have been more vocal. The course ended up being fine; it just had the chaos that first iterations often do (perhaps especially when multiple instructors are involved). It was in that failure to speak, and realization that I should have spoken more forcefully, that I mark as the transition from being a grad student doing DH in a contingent position, to something else.

I’m not sure that the transition matters for anyone but me. (I would be the last person to suggest that graduate students’ efforts are somehow less genuinely DH just because they haven’t yet completed their dissertations). To me, though, it felt like the difference between a dress rehearsal and an official performance. As ephemeral as that distinction is, it mattered to me.

I applied for three positions in 2015. One job I didn’t get, and I’ve never been so happy to have not gotten a job, because the person who got it became a fast friend, and I’m not sure how we would have met otherwise. The second job I didn’t get resulted in a conversation with someone whose work has shaped mine in important ways, and having that conversation was useful and exciting. The third job I applied for is the one I just finished my first month of, as the Digital Humanities Librarian at the University of Miami, and every day of work has felt like a validation of the previous six years’ choices and risks.

As lovely as the fulfillment feels, I’m also aware of things that I haven’t figured out yet — most of all, how to write about what I do, and where that writing ought to go. I’m all too aware of not publishing DH-focused articles in journals or books. Often that’s because the work I do feels fairly personal and sensitive: I work with people, and we work together, but it doesn’t feel right to treat them as case studies or subjects. And on a related note, whenever I’m working with people, I’m also working with institutions (sometimes an institution is a whole school; other times it’s a department, or a college, or a combination of the three), and I’m usually working on figuring out the systems that are in place, how they run smoothly or roughly, and how I can work with them. Often I’m trying to change something about them, or about how people navigate them; and even when I’m making tiny changes, calling attention to them is often the very last thing I want to do. Much of the time, increased attention hinders more than it helps.

That said, I can’t hide entirely behind those reasons for not writing, because I freely admit that I’m uncomfortable as an academic writer. Mainly this is lingering guilt at how long it took me to figure out the genre of the dissertation and write it. I got through, and I learned something, but the lesson still feels more awkward than cathartic. Last spring, I read some articles in journals related to my dissertation research, and what jumped out at me was how small and precise the arguments were, and how much they contrasted with my dissertation chapters, which were like bursting suitcases in comparison. For the first time, I started to be able to think about what smaller and more precise versions of my own arguments might look like, instead of feeling like I always needed to fit more in to be good enough.

In 2016, my goal is to build on that epiphany, both in regards to writing about DH and infrastructure and eighteenth century poetry. My watchword for the year is “revision.” It has direct relevance to writing, but I’m also thinking of it as “re-vision.” Forgive me the cheesiness of it (or don’t — I don’t care!) — but having had the lucky fortune of plans and gambits succeeding, I have a lot of discovering to do about my current environment, and what I might work towards in the future.

This was a watershed year; those don’t happen often. Besides working hard, I was supported by friends and mentors in numerous ways. In having plans pay off, I learned to listen to and trust my instincts* (a significant thing in a world where women are encouraged to second-guess themselves or look to men for guidance.) I don’t know what the future will bring, and a lot of things about it frighten me — but I’m glad to have made it this far.

* This does not mean that trusting my instincts will always lead to success, as I’m well aware.

Yesterday I had the great pleasure of speaking to graduate students at U Toronto as one of the panelists on a DH Roundtable organized by Matt Schneider and Elisa Tersigni. The graduate students asked excellent questions, and I really appreciated the opportunity to think through my own experiences with DH in the past six years. I also got to tour the print shop in the bottom of Massey College, and it was just delightful, and I want to go back, and I am more than a little envious of graduate students who have the opportunity to learn letterpress printing there.

This post is a link dump — I recommended various sites/essays/tools in the conversations after the roundtable, and I thought it would make it easier for people to find them if I collected them here. I jotted down a list of them on the subway back to Union Station, so I think I’ve remembered them all, but if I’ve forgotten something, then hopefully someone will nudge me on Twitter or via email.

TransformDH, which examines issues of Race, Ethnicity, Gender, Sexuality and Class in DH

Brian Croxall’s tutorial for a Simile Timeline exercise; the Simile Timelines assignment I developed for students in one of my courses several years ago; the outcome of that assignment. And Google Fusion Tables, which allow for similar visualizations based on spreadsheets — there’s no timeline functionality, but you also don’t have to precode any HTML.

Bamboo Dirt / the DiRT (Digital Research Tool) Directory, a good source for finding all sorts of tools for digital scholarship.

USC/ANVC’s Scalar CMS, which has really neat functionality in terms of allowing multilinear scholarship, even though I think that its export functionality needs work, and hope that ANVC will keep improving it.

The revised Demystifying Digital Scholarship values (which contain a link to the earlier version)

Model View Culture, a journal about tech, culture, and diversity media.

I will note here, for personal record-keeping as much as anything, that when asked what I wish I had known when I started, I said that I had felt very tentative/uncertain about the value/importance of writing publicly about my work. I now know that said public writing was really important. It helped me find my voice, and develop my own sense of the field of DH, and what I wanted to do. I think, in fact, that blogging and tweeting have been instrumental in helping me make the transition from being a graduate student to a professional academic (in humanities generally, not just in re: DH). Writing in public allowed me to go from being an DH onlooker to being a participant in the larger community of practice.

More on that subject in a future post, I hope. But not this morning.

As a member of the CLIR/DLF Post-Doctoral Fellows program, I attended this fall’s CNI meeting with the rest of the cohort of 1st year CLIR postdocs — a special treat, since attendance at CNI is normally restricted to two people from each of the member institutions, and two people from McMaster were already going.

The CNI meeting is a hybrid of the industry and academic conference styles. This means that in every session I saw, people actually spoke, rather than read papers; and that some sessions were hour-long talks given by one person. The role of the speakers varied: some were industry professionals, others were librarians or alt-ac staff, a few were professors.

It was an interesting conference, and one that I suspect I would benefit from attending again, if I can make it work, because there do seem to be trends that ebb and flow. CNI caters to a particular hybrid community — one which in some ways, I’ve been part of for a few years now, and in other ways, I’m new to, as a recently-minted PhD, and as someone newly employed within a library to do digital scholarship work and development. In short: I’m still learning how to assess the buzz, and judge what might be short-term trend talk, vs. recurring concerns.

With that caveat, here’s what I noticed/found myself discussing with others at the conference, and what I expect I’ll be thinking about in the next several months.

1) Linked open data.

It seemed like there was one session focusing on LOD in almost every time slot. Has LOD always been such a hot topic, I wonder, or are libraries/academic info-professionals getting into it more recently, much as I have? The number of sessions devoted to LOD is a measure of interest in it, rather than clear strategies for working with it/decisions about what to do (but this is a perennial challenge with LOD, or it has been so far — this presentation from Robert Sanderson from the JDH is a good run-down of some of the problems.

I’m still enthusiastic about the potential of LOD for my project, and for digital humanities projects that involve complex, heterogeneous data — but I admit that what I saw (and read via Twitter, for panels I didn’t attend), made me understand Sanderson’s lack of confidence in it as a platform. There seemed to be some instances where people wanted to be extraordinarily precise with their data, and others where automation was going to happen on such a vast scale that errors were inevitable, and it seemed that there would be little effort to even try and deal with them. (I keep hoping that I thoroughly misunderstood what I heard at that particular session).

This isn’t the time or place to fall down the LOD rabbit hole, but listening made it clear to me that I need to make my own adventures and process with semantic web stuff more transparent. There’s great potential for LOD in digital scholarship, if we can work through the associated challenges.

2) How people work together, and/or challenges involved in supporting people working together. 

Some people work mostly independently, others work in teams; but a lot (all?) of the endeavours that info-professionals are currently involved in depend on the success of multiple teams being able to communicate and work together effectively. I typed “teams”, but it occurs to me that in some ways, that’s an oversimplification, because teamwork is very much an industry idea, and I regularly see academics (faculty and staff) bristling at the idea of being on teams.

Data is being created/curated/managed by one group of people by use for another group of people (and yes, these two groups partially overlap), but it’s not always clear how much communication happens between the two groups,  how that communication fits into the workflow, or how much time people spend on trying to make the communication effective, rather than potentially alienating. Inna Kouper, of the University of Indiana, gave a workshop on Data Curation for the CLIR cohort, and brought up a problem that I’ve heard mentioned multiple times lately: bad surveys. To wit, people who send out surveys that take up other people’s time — only the questions in the surveys turn out to be the wrong ones, leading to oh, frak, another survey… Making a survey is easy. Making a really good survey isn’t quite as easy.

Here’s a different example of digital scholarship-related communication, and the way in which it can be tricky: today I met with several members of McMaster’s History Department to informally chat about digital humanities; what they’re doing, what the Sherman Centre is doing, etc. And at one point, someone asked what the Sherman Centre’s definition of digital scholarship is, and after thinking about it, for a moment, I gave an honest answer: that we don’t have a closed definition, and that this, in fact, was far better than having a fully-defined and restricted idea of it. I stand by this, because what I know that my colleagues and I want is for our particular version of digital scholarship to emerge from what we do with other members of the McMaster community. That’s far better than either an idea of DS that simply goes out from the Sherman Centre, or one that simply comes into it from the faculty. This jointly-generated idea will be based on the details of faculty members’ expertise, and on Sherman Centre staff members’ expertise — or it ought to be. But for complicated reasons, working that out can feel a bit daunting — both for faculty (not the History Dept. specifically) and for Sherman Centre staff.

At CNI, this sort of situation seemed like the undertone running through many, if not all, of the sessions I attended.

3) Infrastructure, and how much people need to know about it. 

In the opening plenary session (“A Conversation on the Changing Landscape of Information Systems in Higher Education”, one of the speakers referred to some of the constraints of enterprise systems, prompting me to wonder on Twitter how many academics know what enterprise systems are, and whether it mattered if they didn’t know. One of the exchanges that followed points back to the “how do people work together” theme; the other response put a different spin on it: how much do academics need to know about the tools that they use? “Tools” in this sense doesn’t just mean fancy digital apps beloved by self-identified DHers — one major set of tools are library catalogs. I would argue that at this point, library catalogs are almost ubiquitous computing — we think of them as a tool that we are expert at using, and we don’t think of them as a tool that might be subject to sudden change or fragility.

In another session, debriefing attendees on the Executive Roundtable on Digital Humanities (note: the CNI has just released a report on digital scholarship centres), CNI Director Clifford Lynch mentioned that there weren’t as many calls for infrastructure from humanists.  On Twitter, attendees wondered: how much do faculty need to know about infrastructure? Would such knowledge lead to a situation where everyone ended up arguing about the right sort of infrastructure, and nothing got done? One of my CLIR colleagues pointed out that many humanists are probably used to jury-rigging stuff, rather than trying to intervene in the system, and that rings true for me. But when Lynch said a few minutes later that many faculty “didn’t know how to think through constructing a project in a particular area,” I saw it as an indication that faculty need to know more about infrastructure than they generally do right now.

But what kind of knowledge about infrastructure do faculty (and staff) need? It’s too easy to jump to the conclusion that they need to be able to duplicate the knowledge of industry people, which leads to those sticky debates about whether digital humanists need to learn how to code.

I don’t have a good answer to that question tonight.

In all these discussions, infrastructure tended to mean funding, or the presence of faculty/staff/DScentres — so, also the result of funding. In hindsight, though everyone seemed to be talking about infrastructure, or about working together, few sessions were talking directly about the two together, i.e. social infrastructure. The exception in sessions I attended was Adam Hyde’s talk about Project Tahi, a workflow application similar to Trello and Kanban, but with features developed specifically for academic journal editing. I’m still thinking about an assertion that Hyde made early on, which I’ll paraphrase: that collaboration isn’t just about divided labor, it’s about people working on the same thing at the same time. How often do today’s collaborations — especially collaborations in academia — achieve that?

I’m gearing up for #AcWriMo starting Saturday, November 1st — and doing so has made me realize that I’ve been neglecting this site. Since my last post, I’ve joined the Sherman Centre for Digital Scholarship at McMaster University as a Digital Humanities Postdoctoral Fellow, and as part of my position, I’m going to be teaching a version of the Demystifying workshops that I taught at the University of Washington with Sarah Kremen-Hicks and Brian Gutierrez.

One of the first steps for adapting the workshops has been to look at the set of values informing digital humanities that Sarah and I put together. After all, it’s been over two years since they were written: has digital humanities (or digital scholarship) changed since then? I think so, but I wasn’t sure how it would impact what we’d put together.

Now I have a revised version, though I’m not sure it’s ready for the workshops. My main concern is that it’s too long: where the previous values fit on a single page, the new version is 2.5 pages, single-spaced, and includes a couple of paragraphs providing an introductory context. If I use this version, I’ll need to present it differently, just because it’ll take more time to process. I’m still thinking about that.

Without further nattering, though, here’s the revised version. Feel free to let me know what you think.

Values of Digital Scholarship 2.0

(see v.1.0, written in September 2012 by Paige Morgan and Sarah Kremen-Hicks)

Digital scholarship is being produced in multiple disciplines and subfields. In some disciplines, computational methods feel newer than others, depending on the traditions that have been prevalent. Some of the “new” digital scholarship in English Departments, for example, might not seem nearly as new to people working in Linguistics programs.

There’s a lot of emphasis on specific digital scholarship skills: learning to code, learning to work with a particular tool or program. But there’s no single coding language or platform that encompasses all the practices. Instead, when you’re starting to learn, it’s more useful to examine a list of the values that motivate digital scholarship and make up its ethos.

It’s important to recognize that much of digital scholarship being produced right now is responsive to external situations. One such situation is the lack of funding in higher ed, and for humanities fields in particular. Another factor influencing the prominence of digital scholarship is the lack of traditional tenure-track professorships available, and the hope that becoming proficient with digital research skills will help graduate students and junior academics find more job opportunities inside and outside of the academy.

Arguably, then, digital scholarship is both a set of methods and a movement. Many practitioners are concerned with their particular research area, and with critiquing and improving the university as a whole. This is because practices and tools involved in DS have great potential to impact colleges and universities’ infrastructure. Online courses can make education more accessible when well-designed, but can also become a source of profit that benefits the institution more than its students. Collecting data can lead to new insights, but can also raise ethical questions. Being aware of the “method & movement” aspects of digital scholarship is important because the two can be complexly entangled with each other.

Examples of digital scholarship’s responsiveness can be found in the emphases on using social media tools and making scholarship open-access. Funding cuts have impacted library purchasing budgets and travel funding that facilitated face-to-face dialogue, as well as class size and teaching loads (which impacts both graduate students TAs, and the time that faculty have free to do research and/or mentor grad students). The emphasis on social media tool-use and open-access resources are partly efforts to compensate for these effects.

Adaptive: Motivated by the view that the practices for knowledge production, publishing, reading, and writing, are changing, and that these changes affect the scope of professional academics’ work and responsibilities.

Sustainable/resource-aware: Thinking proactively about how a resource, project, or skill will be used. This thinking involves consideration of what will be necessary for its long-term effectiveness, or might result in planning projects that are deliberately ephemeral, and have a low resource cost.

Multimodal: Involving multiple modes (both image analysis and written commentary, or an essay published  in a format that is designed to be navigated in different ways); produced in multiple or alternative media formats that result in a variety of types of audience engagement.

Interdisciplinary: While interdisciplinarity is not a requirement, many digital scholarship projects borrow or blend aspects of multiple fields. Text and data analysis projects may require statistical knowledge, while other projects blend literature and historical studies with geographical information.

Auto-didactic: Willingness to independently learn a skill or technique that falls outside of the traditional boundaries of your field, and which you may need to teach yourself (or make arrangements to learn). Example: statistical data analysis, programming languages.

Collaborative: The complexity of some research questions may require working with partners who have expertise in other disciplines, or industry partners. A project might begin with autodidacticism, and become collaborative as the project grows in size. Alternately, a project that involves looking at how users interacted with a particular tool, text, or site, would benefit from having investigators from multiple schools or communities.  In addition, many people working with digital scholarship cite early-stage collaboration as a source of richer ideas and even a more fun work experience.

Ad hoc: 1) Experimental, either in pursuit of serendipitous discovery or due to the lack of a single best practice; 2) Focused on solving a problem or meeting a particular need.

Process & product-driven: Traditional products of scholarship (essays and monographs) emphasize final products as valuable. In contrast, digital scholarship treats the process as equally valuable and worthy of commentary and dissemination. This can mean that even failure is valuable, provided that the failure is presented in a way that it can contribute to others’ work. As a result, rather than working towards one final product, digital scholarship is often designed to be iterative: a scalable sequence of small goals that are valuable in themselves, and add up to a larger product over time.

Accessible: Digital scholarship is often linked with the open-source and open-access technology movements, which advocate for making products freely available for use and modification, and for making code that determines exactly how a program functions easily visible. An equally important aspect of accessibility involves making resources (printed, digital, or other formats) fully useable by people who have visual processing disorders or other disabilities that affect their use of resources. The wealth of information available on the web can be made accessible to audiences with varying abilities if content producers are thinking actively about accessibility from the start.

Public & transparent: Public scholarship, which involves and engages people and places outside of the academy, is its own field. It overlaps with digital scholarship for a couple of reasons. One reason is that a common component of digital scholarship is the preservation of texts/objects/information that is already of interest to the public; another is that public scholarship allows for greater advocacy of the importance of scholarship in general.

Another aspect of the public practices associated with digital scholarship is public peer review, which allows for more diverse commentary on scholarship prior to publication, and allows readers to see how feedback has shaped scholarship.

Project-oriented: This value may change, depending on whether digital scholarship becomes more integrated into departments, and on the availability of training — but for the time being, digital scholarship is substantially project-based: you set a goal, determine how to accomplish the goal, document your results (whether success or failure), and disseminate them to an audience. The scope and type of project can vary: one project might go on for years, while another is a month’s work. Your project might be about analyzing a set of data with a particular tool, researching potential audience engagement, or learning a new skill (i.e., using Tumblr).

Social: Who else is working on topics related to yours? Who will the audience for your work be? How will you reach that audience and build interest in your project(s)? What might you discover when you start communicating your goals? These are all questions that you might ask as you start learning about digital scholarship, and especially if you begin planning a digital project. Being a digital scholar doesn’t mean you have to be a social extrovert — but finding ways to communicate early on (when you’re starting and developing your project, as opposed to when it’s all done) can make the difference between success and failure. And while this type of communication can sound like just more work, the audience you build can provide key support as you experiment.

This list of values isn’t a rulebook, or a manifesto. Each of the values is subject to ongoing discussion among people who are doing digital scholarship. Moreover, each value is highly likely to evolve in practice over time. Some projects and practitioners are more focused on certain values, while less focused on others. With that said, each of these values tends to work best when used in conjunction with others, rather than in isolation: ad hoc experimentation isn’t sustainable if you’re not making an effort to document your process, and share it with others who can use it.

I was tweeting about this, and it occurred to me that it would make a good short blog post. Here it is:

Do nothing in isolation: always connect the events.

If you have a guest speaker talking about a particular platform/technology/project, consider offering a Very Short Introduction to their work/the platform/project. By very short, I mean around 45-50 minutes.

The week after the guest speaker’s talk, aim for a similarly short participatory session. The focus could be something along the lines of “Guest Speaker was working with Omeka/Neatline, so let’s look at the bare minimum that you’d need if you wanted to work with Neatline in Omeka.”

Follow that up with a session where people bring a tiny bit of data/material that they want to work with.

Avoid the temptation to focus on leveling-up. That can lead to a steep slope that excludes people as soon as they’ve missed one session. Instead, aim to create multiple entry points into the process of learning the platform. Depending on the project, depending on the learner, the entry points may be significantly different.

Do nothing in isolation is my primary rule because returning to an idea or tool — practicing it — is central to learning. Many people know this — but making time for practice, especially in our current overscheduled culture, can be extraordinarily difficult. I would argue that finding occasions for practice are the most challenging hurdle that new-to-DH learners face. Some people will disagree with me, arguing that “if someone really wants to learn, they’ll find a way to commit to it on their own.” They’re not necessarily wrong. I’m just not a fan of the “survival of the fittest” as a pedagogical philosophy. The more I build things in code, the more it prompts me to think about how we arrange educational experiences. What I’ve suggested above is only one specific approach — depending on your population, and the resources available, the types of events you create may be different.

How easy is it to connect the dots, er, events? Not too difficult in one sense, because the idea is not to overwhelm people. Instead, you’re being proactive about managing the experience of being a new learner. The content isn’t rocket science, or it shouldn’t be.

However, neither is this the sort of planning you could do in your sleep, or in that spare 5 minutes of the day. It requires planners to be alert, and to think carefully about the building block sessions that support bigger events. How might you create a series of short building block sessions that progress between two speakers, and make a tacit connection or contrast explicit?  What sorts of short handouts/videos/etc. might you put on your central website in between these sessions? How do you ensure that your learners know that you’re trying to create multiple entry points, so that they don’t assume that they’re being excluded/don’t know enough to participate? That’s work. It can be incredibly functional work, though — both for the target population, and for the planners, who are essentially finding ways to lightly teach the subject matter, and thus learning (or re-learning) it themselves.

Posting here has been light this summer as I have been freelancing, temping, revising articles to submit for publication, etc. That trend of light posting is likely to continue for a little while, but with the diss out of the way, I’ve been working more on Visible Prices, and posting project-related updates there.

See also: Part 1.

Since writing the first part of my response, I have:

1. participated in the “DH for Chairs and Deans” workshop at DHSI, facilitated by Ray Siemens and John Unsworth; and
2. attended a plenary session, and follow-up discussion on the spectrum of graduate student employment at the ADE/ADFL meeting; as well as having the opportunity to talk about DMDH with attendees during an informal coffee hour.

Several people wondered why I took a course on digital humanities for administrators. Here’s why: my experience as a practitioner, building projects from scratch, is only one angle on the field. If I’m going to teach new courses and create new programs, then what I’m doing is infrastructure development. Infrastructure and logistics are the province of administrators – and thus, it’s vital that I understand their perspectives.

I’m going to be circumspect about the ADE/ADFL workshop in this post, but I came away from it hopeful, and with three specific impressions:

• People are thinking about the challenges of time-to-degree and the job market from a number of different angles, depending on their background and experience.
• The problems of PhD employment and graduate education are national/international, but the solutions are often highly local/regional.
• The attendees of the ADE/ADFL are all too aware of the fact that many aspects of the reforms proposed (as well as new aspects of humanities scholarship generally) are a hard sell; and they are trying to find a way to make them an easier sell; or at least, to lessen the chances of people dismissing new ideas right from the start.

The second of these impressions is worth more discussion – but I’ll save that for future posts.

I left feeling encouraged about the dialogue that the Report was promoting, and grateful to the Task Force for the work that they put into creating it.

The first part of my response was about concerns with the Report’s framing of graduate students as learners and departments as authorities in the context of new modes of scholarship. This second part is about concerns with logistics.

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I started learning how to be a graphic designer 5 or 6 years ago, when I started helping out the UW Textual Studies Department with promotional posters for visiting speakers. Visual design has a definite learning curve (as does working with Photoshop and InDesign, my preferred tools) — the best way to get better at it is to design things. I haven’t always had time to do so — none of my official positions call for me to design steadily — but I like to think that I’m becoming fairly good at it, for an autodidact.

I also think that graphic design is an important subject for digital humanists to be mindful of. This is partly because often enough, we’re making visual objects — and there are aspects of user interface design that have less to do with tech, and more to do with thinking about readers. I also believe that design is how we communicate with each other. Thus, it’s not just that a picture is worth a 1,000 words; it’s that sometimes, a picture is much more effective than 1,000 words. This is especially worth considering in academic settings, where I see lots of people experiencing text fatigue.

You don’t have to take an expensive class to start thinking about design, either. One of my favorite sources for guidance and inspiration is graphic designer Chip Kidd’s latest book: Go: A Kidd’s Guide to Graphic Design.

This spring, my colleague Tyler Fox and I tried out holding Digital Humanities Office Hours on alternate Thursdays. Office hours are one of the services that participants in the Demystifying Digital Humanities workshops consistently ask for — but we didn’t get the participation that we’d hoped for. Part of that is spring quarter fatigue, but I also think that it’s because I didn’t promote the events very well — and in anticipation of trying again this fall, I whipped up the following images last night.

In digital humanities, it is especially important to acknowledge your collaborators, and to convey the work that went into a particular project. I think that the same applies for Ph.D. dissertations, and thus I am very pleased to include my formal dissertation acknowledgments on this site.

Acknowledgments

This dissertation would never exist without guidance from Marshall Brown, who did not hesitate to tell me when something wasn’t working, and was equally jubilant when something was working. He taught me much of what I know about academic writing, but more importantly, he helped me become confident enough that I could teach myself. Just as important were his insights and encouragement regarding eighteenth-century poetry and constructing an argument that often felt like a daunting uphill slope. In short, Marshall’s guidance was perfectly suited to my strengths and weaknesses; and I haven’t even attempted to describe everything I learned at MLQ. I hope some day to achieve something like the mixture of grace and ferocity that characterizes his scholarship, editing, and teaching.

Brian Reed taught me to see my experiences as a writer, researcher, and professional academic in a larger perspective than I had on my own. He helped me to see how what I was learning – about poetry, digital humanities, teaching, writing, and project development – might fit together in different ways as I planned for my career as an academic. Undertaking a Ph.D. and an academic career are activities that are in some ways akin to sailing out into an unknown ocean, and without Brian’s wisdom and kindness, I might never have learned how to steer.

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