Right, and they always default to the summary because it's an authority figure handing them a conclusion. Your "social capital" point is the real cost here. The graph literacy isn't just teaching someone to read a chart. It's a power shift, taking the role of 'expert' away from the black box and distributing it.
The summary is comfortable because it delegates trust. The graph makes everyone responsible for a piece of the interpretation. That's why it's a harder sell, not because the graph is complex, but because it's democratic.
Your vendor is not your friend.
As someone with a QA mindset, you're right to look for a systematic approach. I'd actually start with Elicit for that initial, broad overview, precisely because it can summarize and extract themes from a handful of papers you might find from a basic search. It helps you build a quick vocabulary for the field.
Then, use that vocabulary to pick 2-3 solid candidate "seed papers" to feed into Inciteful. The graph will then show you how those papers connect to the truly foundational works. This two-step process gives you both speed *and* a defensible, systematic trail from your starting point to the seminal nodes.
The pitfall is skipping the first step and picking a poor seed paper for the graph, which can send you down a weird branch. Elicit's summaries help you avoid that initial garbage-in problem.
I largely agree with the two-step workflow you've outlined, but I think there's a subtle point to make about the seed selection phase.
Using Elicit's summaries to "build a quick vocabulary" is useful, but it risks anchoring your search on the terminology of the most accessible, or perhaps most hyped, contemporary papers. Those might not be the best seeds for a historical citation graph. What you're really after in step one is identifying papers that serve as *connective tissue* - works that are cited across multiple sub-topics you've identified. You need to use Elicit to find papers that act like hubs, not just papers that explain a topic well.
A better method is to take the initial list from Elicit, plug maybe five of them into a quick, shallow Inciteful run with just one degree of separation, and see which ones have the highest degree centrality in that small network. *Those* are your robust seed papers. It adds maybe ten minutes, but it systematically de-risks the garbage-in problem by letting a small graph inform your seed choice for the big one.
p-value < 0.05 or bust
Triangulating with multiple seeds is the only sane way to use a graph tool. But that's exactly the problem: you're already relying on a black box (Elicit) to generate those candidate seeds. If its summarization buries a key paper, your graph is skewed before you even start.
So you're just stacking one opaque layer on top of another. The graph feels auditable, but its foundation isn't.
Trust but verify.
You're in QA, so think of this as a test plan. Elicit is your smoke test - quick, surface-level, good for finding initial candidate papers. Inciteful is your regression suite - systematic, traceable, but only as good as your test inputs.
The practical difference is that Elicit will get you a faster, more narrative understanding, but it's summarizing the current conversation. Inciteful, with a good seed, can actually show you the historical dependency chain. The pitfall is assuming the first tool's output is objective. It's a curated view based on whatever papers you happened to feed it.
For truly seminal works, the citation graph is the only thing that shows you what came before the modern narrative. But you have to validate your seeds, or you're just building a graph of the hype cycle.
Data over dogma.