Epistemic Labor at Scale: Access Services as Practitioners of Precision, Presence, and Partnership

I’ve been thinking about the individual experience of students who become scholars through repeated acts of recognition, and the necessity of scale. I’ve had the good fortune to have the experience of working in places that can be very high touch and in institutions where you have to start with scale first and find ways to come back to individual relationships. My experience shows there are things we all can learn from organizations facing different approaches to the scale challenge. What is true at all of these institutions is that the answer is never exceptional staff members or individual priorities, but rather the architecture of our approach. The infrastructure of belonging is based in how public services is organized, trained, and philosophically oriented. When I spoke at the 2025 Access Services conference my keynote was titled The Foundational Leadership of Access Services: How Service Builds Institutional Possibility and we reframed the precision, presence, and partnership that public services offer as acts of leadership, and today I am also thinking of them as epistemic practices.

The vocabulary problem: “support” obscures what’s actually happening

Public and Access Services work is often framed as support. As someone who started her career in this type of work, I don’t think most people were thinking of it as “supporting cast” but rather “critical support.” And yet I know that words matter and the work “support” is a word that can easily place these roles outside of the epistemic function they actually enable. Support can be seen as peripheral, supplementary, downstream. Infrastructure is load-bearing. The distinction matters institutionally because what you call something shapes what you’re willing to invest in, who you think can lead it, and how you measure its value. When I was thinking about the frame “warehouse versus infrastructure,” this is the way it can show up when talking with people who don’t understand the role of libraries in 2026. They’re busy thinking of books, gate counts, and the library as a place that takes resources and stores things. Anyone actually working in a library knows we’re enabling the work of learning and knowledge production. Yes, that is done through collections and useful spaces, but it’s also the consultations, services, collaborative environments that we create and a cultural position of valuing and amplifying our institutions’ scholarly contributions. The work that contributes to these, as the work of access services, is not support but strategic infrastructure.

Precision as epistemic practice

Precision in public services means knowing enough about what a patron actually needs (as distinct from what they asked for) to respond usefully. That’s not customer service; it’s a form of situated knowledge-making. The public services professional who anticipates the need behind the request, who knows the difference between a student who is lost in a database and a student who is lost in the institution, is doing interpretive work with real epistemic stakes. Library science programs teach the craft of the reference interview, a framework for understanding this interpretive work. Many access services staff have developed the same capacity through sustained attention to the people in front of them, arriving at the practice without the vocabulary for it. The work is the same; the paths to it are different. When this work happens in access services, staff document procedures, preserve institutional memory, and build systems that help others know what the answers are (and maybe even where they came from and why). This is Haraway’s situated knowledge in organizational form.

Presence as epistemic practice

Presence is the capacity to actually see what’s in front of you, to read uncertainty in real people rather than in abstracted queries. AI performs retrieval and synthesis, and it can be very good at this. It cannot perform presence in the way the work requires. It cannot notice that someone is overwhelmed rather than confused, or that the real question is different from the stated question, or that this moment calls for de-escalation rather than information. What access services professionals do when they read the room, as they adapt, calibrate, and respond to what is actually happening in front of them, is precisely the thing AI cannot do and performs as though it can. AI’s performed epistemic humility responds with certainty. Even when it expresses doubt, it does so in a way that is overconfident. I routinely find myself getting a reasonable answer but knowing it’s wrong, reporting that it is, and getting an answer that says something to the effect of “oh, of course! now that I think of it…” but I only had the certainty to push back because of decades of thinking about the topics we’re exploring. Students, in particular, don’t have that intellectual scaffolding in place quite yet, it’s part of why they’re there! The human that is present with them can see hesitancy and read the situation to adapt in a way that is genuinely useful for learning.

Partnership as epistemic practice

Partnership is the long-term orientation. It’s the understanding that creating a knower isn’t transactional but relational, and it happens across time and repeated contact. Pugh’s connective labor has become a really important framework for me. It’s the idea that there is important work in seeing another person in their specificity and forging emotional understanding with others. This is the work of building relationships through which inquiry becomes possible, and is what I’ve been talking about in the last few posts. In my broader epistemological frame, partnership also means something institutional: the library is a core partner in the university’s knowledge mission, not an auxiliary to it.

Precision, presence, and partnership happen all the time, in public services work. In every interaction, in observed interactions, in just the physical presence of a caring staff member this work impacts all students who pass through and use the library. The architecture of these service experiences is how this epistemic practice and leadership scale well beyond what one might otherwise expect. This work scales because of what surrounds each interaction: the hiring that selects for people who can read a room, the training that treats interpretive work as a competency, the documentation that lets one staff member’s insight become another’s starting point, the shared orientation that makes precision and presence feel like the job rather than extras added onto it.

If precision, presence, and partnership are epistemic practices, they require investment of a different kind than service competencies do. You don’t evaluate epistemic labor the same way you evaluate service provision. You don’t staff it the same way. The answer is hiring, training, culture, documentation, institutional memory, and shared philosophical orientation. And the shifts necessary to prioritize the epistemic frame may run counter to the efficiency answers that most institutions have been pushing toward over time. Every investment is an exercise in prioritization, and when determining how to support developing scholars and belonging, this should be an area to consider.

In my last post I was really thinking through what it means to create a knower. Today I’ve been thinking about the institutional infrastructure through which that creation happens not once but thousands of times, distributed across a function that has rarely been named in these terms in institutional leadership conversations. When universities ask what libraries do, access services is usually the last thing they think to examine; in reality it should be among the first.

This is a post in an ongoing project exploring libraries, knowledge, and the epistemic stakes of artificial intelligence. I’m drawing on social epistemology, feminist theory, and two decades of practice in academic libraries.

When ‘Probably’ Means Nothing

When I moved to the Pacific Northwest I was surprised how much people volunteered to me that they loved the Southern word “y’all.” It’s a great inclusive way to call a group together or refer to a team. I love it, too. But my favorite Southern phrase is “might could.” It’s double-hedged, which may appear to be redundant or imprecise, but actually it’s the opposite. It’s a finely calibrated expression of a qualified possibility that a single modal can’t quite capture. “Could” alone is too open. “Might” alone is too tentative. “Might could” lands somewhere specific that neither word reaches on its own. It’s also situated. You know something about the speaker when they say it. It carries place, community, a whole set of social relations. Which is exactly what Haraway is talking about in situated knowledge.

Hedging language can be perceived as negative or as an indication that the speaker isn’t confident. But in academic circles it often is interpreted as a signal of some epistemic humility or recognition that the concept has enough complexity that you need a bit of hedging to remain accurate. When a scientist says “probably,” a doctor says “likely,” a colleague says “I’m fairly certain,” those words are doing the real epistemic work of communicating a speaker’s actual relationship to uncertainty, calibrated by experience, context, and stakes. It’s worth reflecting on what is lost if these turns of phrase are stripped of their nuance.

When I read ‘Probably’ Doesn’t Mean the Same Thing to Your AI as it Does to You, I was struck that our LLMs may not be using hedging language in the way that we do. LLMs use words like “probably,” “likely,” and “almost certain” inconsistently, averaging over conflicting usages in training data rather than assessing actual odds. The article also points to an interesting intersection with gender studies, showing that the same probability expressed differently depending on whether the prompt says “he” or “she.”

This is a really specific kind of epistemic failure, and an interesting one! Hedging language is how knowledge communities signal the limits of what they know. Strip that calibration out and you get fluency that performs humility while enacting the view from nowhere. This is Haraway’s god trick at the lexical level. We’re moving beyond the synthesis of sources and into in individual word choices.

We’ve all seen use cases in which AI in increasingly being used to summarize research, brief decision-makers, and mediate information. We also are all aware of the conflicting views on to what extent that information is actually good. For now, at least, it seems that we may also have to consider the word choice itself. When the methods we have to convey certainty lose their clarity we may find ourselves being overconfident in our interpretation of words, only to find we’ve made decisions without the information we assumed was supporting our path. Things appear as they were, but in reality the world shifted around us. We read “probably” and think we know how confident to be, but the word has already lost its weight.

The Obsolescence Argument Has It Backwards

Everyone seems to agree that artificial intelligence is going to change education, research, and libraries. The disagreement is about direction. The dominant narrative, at least in some technology circles is: AI can find information, synthesize sources, and answer questions. It’s not a surprise that people hearing that argument in media and from tech commentators point out that libraries and librarians do those things and then assume that libraries are in trouble.

But to anyone who sits at the intersection of technology and libraries it’s abundantly clear that AI doesn’t make libraries obsolete, but rather it makes them more essential.

I’ve been thinking about knowledge systems for a long time. My undergraduate degrees were in philosophy and in communication, with a minor in Women’s and Gender Studies, and the questions that animated these fields were the same ones: Who knows? Under what conditions? With what authority, and on whose behalf? Those questions led me to library science, and they’ve shaped how I’ve understood this work ever since.

Two frameworks have always been particularly generative for me. The first is social epistemology. This term was developed by Jesse Shera and Margaret Egan in the mid-twentieth century, which understands libraries not as warehouses of information but as infrastructure for how communities produce and share knowledge. Libraries, in this view, are epistemic institutions. They don’t just store what we know; they shape the conditions under which knowing is possible. (Incidentally social epistemology also developed within Philosophy, with a slightly different implementation, a few decades later.)

The second is feminist epistemology, particularly Donna Haraway’s concept of situated knowledges. Haraway’s argument, made in a landmark 1988 essay, is that all knowledge is produced from somewhere: from a particular body, a particular history, a particular set of social relations. Claims to view-from-nowhere objectivity, what she calls the “god trick,” are not neutral. They are themselves a kind of power move, one that erases the conditions of knowledge production and forecloses accountability. Sandra Harding’s standpoint theory extends this: knowledge produced from the margins, from positions of accountability rather than dominance, is often more comprehensive, not less, because it cannot afford to ignore what the center takes for granted.

These frameworks were developed to critique science. But you can see why I keep coming back to them today.

Large language models perform exactly the god trick Haraway identified. They synthesize at scale without provenance. They produce authoritative-sounding outputs whose origins are opaque, whose training data encodes historical power imbalances, and whose confident tone actively discourages the epistemic humility that good inquiry requires. They are, in Harding’s terms, knowledge produced from nowhere. And this means they are making claims from a position that cannot be held accountable.

This is not primarily a technical problem. It is an epistemic one. And it is precisely the problem that libraries, at their best, are structured to address.

Libraries curate situated knowledge. They preserve provenance. They maintain the bibliographic infrastructure that allows a reader to ask: who said this, when, from what position, in conversation with whom? They select, describe, and organize materials in ways that make the conditions of knowledge production visible rather than erasing them. They employ people (librarians!) whose professional expertise is not only finding information but teaching the critical practices that allow communities to evaluate it.

None of that is replicable by a system that has been specifically designed to flatten those distinctions into fluent prose.

I’m not arguing that AI is useless, or that libraries should resist it, or that the landscape isn’t changing. It is changing, and libraries need to engage with that change thoughtfully and without too much nostalgia. What I am arguing against is the idea that AI supersedes libraries. When someone asks whether AI makes libraries obsolete, the questioner implicitly accepts a definition of libraries as information retrieval systems. That is a definition that was always reductive and is now actively misleading. Libraries are epistemic infrastructure. They are, in Shera and Egan’s terms, the social mechanisms through which communities organize their relationship to knowledge.

AI doesn’t replace that. It creates new urgency for it.

The more our information environment is shaped by systems that perform objectivity while encoding power, the more we need institutions committed to making those dynamics visible. As synthetic text becomes more fluent and authoritative, it will become more important for human thinking to maintain the skills in citation, provenance, critical evaluation, and the slow work of understanding where knowledge comes from. These are the skills that libraries cultivate.

The obsolescence argument has it exactly backwards. This is the moment libraries were built for.

This is the first post in an ongoing project exploring libraries, knowledge, and the epistemic stakes of artificial intelligence. I’m drawing on social epistemology, feminist theory, and two decades of practice in academic libraries.

Before we begin

Years ago I kept a blog (at this URL, even!) where I thought out loud about libraries, knowledge, and the profession I’d built my career around. I was good at it for a while, and then I wasn’t, and then I stopped for all the usual reasons: changing life phase, less personal time to spend on it, increasingly demanding institutional role, the way the platforms evolved from places of earnest and open discussion… I drifted so far away from blogging and this website that when a back up didn’t capture all the files I wasn’t even all that disappointed.

But lately I’ve really missed thinking in public with other colleagues interested in exploring the same ideas. And lately I’ve been thinking a lot about academic libraries, our information environment, and the ways we talk about and use artificial intelligence.

AI is reshaping how people find, evaluate, and trust information. Within libraries we have people all across the spectrum: from those who fully embrace it to those who believe it has no place near our work. One of the dominant narratives outside of the profession treats libraries as information retrieval systems and concludes that AI makes them redundant. This framing mistakes the symptom for the disease. Libraries are epistemic infrastructure. They are the mechanisms through which communities organize their relationship to knowledge. AI doesn’t replace that, but it does make that role all the more urgent.

This lens keeps coming up for me in conversations in varied spheres. Jesse Shera and Margaret Egan’s social epistemology, which understands libraries not as warehouses but as institutions that shape the conditions under which knowing is possible, is foundational to how I think about this work. So is feminist epistemology, particularly Donna Haraway’s concept of situated knowledges and Sandra Harding’s standpoint theory. These frameworks were built to interrogate science. But it turns out that they are extremely useful when interrogating AI as well.

I’m writing as a person who has spent two decades in academic libraries and who has been thinking about knowledge, power, and institutions since an undergraduate philosophy degree made those questions unavoidable. At this URL, I am not writing as an institutional voice. This is a thinking space. I’m hoping that arguments will develop, get complicated, and occasionally get revised. I expect to adapt to new information.

What follows this post is the first real argument: why the obsolescence narrative has it backwards, and what a clearer account of libraries and knowledge reveals about the epistemic stakes of this moment.

I’m still trying to understand where people talk about these things today. In some ways everything was a lot cleaner when the answer was a blog with open comments, an RSS reader, and Twitter. The messiness of our knowledge environment today (LinkedIn? Bluesky? Mastodon? SubStack? Chat threads? Everywhere?) resonates with the messiness of the information ecosystem I’m trying to write about.