Week 5: From Philosophies of Existence to the Perfect Grant Proposal
DH510 - Class 5 - 2025-10-07
First, the good news: Literature Review (LR) feedback and peer reviews are coming back this week. I’m anxious to see what everyone thought. Then there’s the last Evaluative Critique (EC4) due next week, where we have to score and critique two major, funded Digital Humanities grants—one on data bodies, the other on trans game art. The main point is to focus on the structure: the project description, expected outcomes, and how they plan to mobilize their knowledge. Honestly, no one expects us to fully grasp the high-level concepts, just to show our own critical position.
The big new task is Grant Proposal 1 (GP1), due in two weeks. This is basically a Notice of Intent, structured as a one-page summary (3,800 characters max). The goal is to take my LR topic and propose a project that would genuinely expand knowledge in that area—imagining I just got $75,000 in funding. Thankfully, we’re allowed to self-plagiarize and use language directly from our LR for this summary. Next week’s class will actually be a grant-writing boot camp, and we’ll even have a recent alum come talk to us about current grant and doctoral scholarship opportunities.
The final administrative nugget was about the Meta Review. For a submission I didn’t personally review, I have to read the reviews written by others and summarize their notions in a brief paragraph. It’s a great exercise in critical reading of feedback itself.
How Do We Even Know Things? (Epistemology 101)
We spent a good chunk of time diving into the philosophical foundations of research, which is essential before building any DH project.
The discussion covered the classic trio: Epistemology (the theory of knowledge—how we know what we know), Ontology (the theory of existence—what exists and what categories it belongs to), and Methodology. The main takeaway is that in advanced academic work, these terms aren’t monolithic; their definitions are often contextual, depending on the field you’re in.
Every single research project starts with an ontological commitment—a specific interpretation of what reality is. This is the opposite of the positivist approach (often associated with hard science), which assumes one external, shared reality based on generalized rules.
We also broke down different approaches to qualitative research:
Grounded Theory: This sounds fascinating—it’s a bottom-up process where you develop your theory from the data you collect, rather than starting with a hypothesis.
Ethnography: Getting embedded in a culture or community to collect detailed data.
Feminist Methodology: This emphasizes the crucial need to deconstruct the ways that historically patriarchal traditions have structured scientific inquiry itself.
We briefly discussed a fascinating article on creating a 2D side-scrolling video game adaptation of Shakespeare’s Macbeth. The authors essentially presented the process as a generalized “recipe” for adapting literary sources, framing the project’s output as a pedagogical tool to get people to engage more with Shakespeare.
DH Projects: Curation, Code, and Complexity
To ground the theory, we analyzed several actual projects featured in Reviews in Digital Humanities to see how they blended methodology and design:
Imagined Homeland: A mapping project that uses GIS and textual analysis to show Dominican literary geographies. The big problem noted? Sustainability and broken archival links.
Queer Digital History Project: An archival project documenting queer online spaces before 2010. It uses both ethnographic and archival methods, relying on user-contributed media.
19th Century Disability: A visual Omeka exhibition focusing on destigmatizing disability. The irony? The site lacked alternative text for its images, which severely hindered accessibility—a vital consideration that was missed.
Overall, many DH projects rely on curatorial and collective methodologies to make documentation visible and accessible. It’s a good reminder that our work is fundamentally about gathering, structuring, and sharing.
AI: Your Research Assistant, Not Your Ghostwriter
We spent the rest of the class discussing the elephants in the room: Gemini, ChatGPT, Claude, and the like. The professor laid down clear, pragmatic guidelines for using AI tools:
Acknowledge Everything: If you use an AI for anything (even for summarizing in a literature review), you must notify the instructor.
Maintain Intellectual Boundary: Your written work has to be in your own words. The AI cannot replace your learning or speak for you, as that completely undermines your educational progress.
Use it as a Research Assistant: AI is great for preparatory work, summarization, categorization, and initial literature review support.
Validation is Mandatory: AI is notorious for hallucinating sources—creating fake case law or irrelevant references. You must validate that the information is correct and traceable to real sources. (Tools that provide a full audit trail are highly recommended.)
Beware of Technical Hallucinations: AI is often unreliable for complex programming, quantitative analysis, or wrangling specific data; it might give you fundamentally incorrect technical paradigms.
Basically, AI can help you organize your work schedule and summarize a source, but it cannot do your thinking for you. The boundary between the tool and your thought needs to be crystal clear.