Not Just Farming: How an LLM Helped Me Study Saskatchewan’s Smallest Settlements

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Jessica Jack with Chloë Farr and Jacob Polay

This post is part of a series on AI and Collaboration.

Sepia photo of a large group of people in dark suits and hats facing away from the camera, toward a large building. There is a large stone building under construction on the right side of the image.
Auction of unclaimed express goods at Regina City Hall. 1906. City of Regina Archives Photograph Collection, CORA-RPL–B-525. Public domain.

Good AI-assisted historical research isn’t a shortcut around other people’s labour. It’s a way of putting that labour into conversation. In comparison to the extractive practices for which some LLMs are used, some historians have used LLMs to be a medium of research collaboration.

What does that look like? Take one finding from my research into Saskatchewan’s settlement history. For decades, the province’s 429 settlements as of 1921 were sorted by population size. Larger often meant more important.

Sort those same settlements by what they did and the picture changes. I classified them by economic function rather than population. Farm villages, the smallest settlements tracked in the census, had been long assumed to be purely agricultural. However, tracking economic function showed 79% of the classifiable workforce held jobs that had nothing to do with farming. They were grain buyers, bank clerks, blacksmiths, and station agents. Whole local economies built around moving goods and money were hiding inside these smaller places. Settlements dismissed due to their population size were, in many cases, running a meaningful share of the province’s day-to-day commercial life.

That finding didn’t come from one dataset. It came from me putting several separate sources into conversation, each built by different people for different purposes. Census enumerators recorded, and archivists later digitized, the occupation of nearly every working person in Saskatchewan’s 1921 census: roughly 760,000 individual records, built for a government count. A group of university researchers then cleaned, digitized, and mapped those census results and made them available for others to use. A separate research project had compiled grain elevator licensing records, meant to track industry activity, not classify communities. Government historians had preserved records of every post office opened and closed across the province. Railway historians and GIS specialists had digitized rail lines station by station. Finally, hundreds of volunteers, local historical societies, and archivists across Saskatchewan contributed to over 400 community-written local history books, supplying what the official records alone couldn’t: what people in a place actually said the place was for.

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Can You Read Two Million Pages? An LLM Can Help.

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Jacob Polay with Chloë Farr and Jessica Jack

This post is part of a series on AI and Collaboration.

Black and white photo of hundreds of stacked, labeled boxes, viewed from above.
Storage of IBM record cards at the Federal records center in Alexandria, Virginia, November 1959. US NARA 64-NA-1715. Public domain.

Researchers often face a dilemma when working with digital archives. Once they establish their research questions, they scour the web for digitized archival material, often finding thousands of sources. Next they are faced with the daunting task of turning this diverse array of data, composed of tables, ledgers, letters, diaries, bills, and government acts, into one set of relationships that can answer historical questions. Algorithmic transcription and entity tagging help solve the first two steps of creating these relationships. These methods allow the computer to read the sources and organize their information into relevant entity groups to answer the researcher’s questions. 

However, these helpful technologies still leave one crucial hurdle that must be overcome. A corpus of 20,000 documents is still 20,000 disconnected files. A search bar can only find specific words or phrases. Context is missing. It cannot answer the questions historians actually ask about the relationships between actor and entity: who traded with whom, who went where, what replaced what, and what came from where. Answering these questions at this large archival scale is much easier and faster with machines, but requires teaching the archive to hold its knowledge the way historians do. Three connected technologies now make that possible, made even more accessible with the use of an LLM.

The first technology is one that humanists have been building toward for decades. Knowledge graphs store information not as tables but as relationships: entities become nodes, and the connections between them become typed edges: sugar GROWN_IN Jamaica, sugar HARVESTED_BY enslaved Africans, sugar EXPORTED_TO London. This technology may sound familiar. It’s the same structure powering software like Uber, AirBnB, and eBay, and is also the foundation of Linked Open Data which is the basis for Wikidata, Geonames, the LUX: Yale Collections, and others. These open sources publish entity-relationship data with shared identifiers so that the historical spelling “Barbadoes” in one online collection resolves to the same island as “Barbados” in others. A knowledge graph is essentially this idea at the scale of a single project: a map of every documented relationship in your corpus, queryable in milliseconds.

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Stop Choosing Tools. Start Building Them.

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Jacob Polay with Chloë Farr and Jessica Jack

This post is part of a series on AI and Collaboration.

Archive boxes by Pexels. Free for use under the Pixabay content license.

Transcribing archival documents is an important part of historical research, but a transcribed archive is still just prose. Before a historian can map trade routes and build databases to trace a merchant across ten thousand pages, they must turn this prose into useful data, identifying and tagging the persons, places, organizations, and other relevant entities within the text. Doing this by hand at a useful scale is impossible. Instead, historians most commonly turn to software called Named Entity Recognition (NER), which powers most of the structured historical data you’ve ever queried, from tagged court records to place-name indexes.

NER has evolved through three generations. The oldest systems were rules-based: using sophisticated, frequently hand-curated, gazetteers that matched text against lists and patterns. The second generation transformer models used the context of the sentence both before and after each word to distinguish that word’s meaning. For example, these systems could separate “Turkey” the place from “turkey” the animal by identifying context clues, like reading “Istanbul” or “feather,” in the sentence around the word. The newest generation uses large language models (LLMs), colloquially known as AI, which bring general linguistic knowledge to the task. This generalized knowledge allows these models to perform at a higher standard, providing more accurate results when identifying historical entities. Each generation improves on the last, but all share a limitation in that each model performs only as well as its training data resembles the documents under analysis, and almost nothing is trained using historical documents, especially ones as old as the early modern period.

The consequences of this lack of historical training are significant. To measure NER performance, I ran one hundred pages of OCR’d early modern text through fifteen systems and compared every tag against a hand-tagged gold standard covering four entity types: people, places, commodities, and organizations. spaCy, the free tool commonly used by humanists, missed most of what matters and scored just 20 percent on the standard accuracy measure. General LLMs like Google’s Gemini 3 Pro performed better, reaching 67 percent, but its failures are telling. Gemini hallucinated over 450 false entities, manufacturing patterns that do not exist in the sources. Other typical errors included entities filed under the wrong type entirely, such as reading the “East-India Company” as the place “East India.” These errors are not random noise that washes out at scale; they’re systemic, mis-tagging the same items the same way every time.

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The AI-Enabled Boom in Document Transcription

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Chloë Farr with Jessica Jack and Jacob Polay

This post is part of a series on AI and Collaboration.

Colour photograph of old hardback books on a shelf.
“Old Books” by jarmoluk via Pixabay. Free for use under the Pixabay Content License.

For many people who use archives, the accessibility of archival material is a notable difficulty. Archivists and librarians have long been turning to software to help with that accessibility. One of their main tools is Optical Character Recognition (OCR). This type of software turns images of text into digital characters, which can then be accessed by anyone with an internet connection and can be easily processed and analyzed outside of an archive. But OCR started as a corporate solution to corporate problems, which meant it was not very good at dealing with the differences present in archival materials. Archivists have been trying to address this with bespoke technology, but the last few years have presented a new way to deal with the problem.

OCR enabled by AI vision language models (VLM-OCR) has recently exploded as a research topic in multiple fields including computer science, digital humanities, library information science, linguistics, and beyond. This new movement in OCR began because AI developers hit a wall. They had trained their models on all available data on the web so they tried using AI-generated data instead, but the results were problematic, inconsistent, and risky. To solve this, they turned to physical documents to broaden their training base. However, they needed better OCR to make this possible. The paper by AllenAI that opened the field, “olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models,” says as much in its opening lines: PDFs hold enormous volumes of “novel, high-quality” training data, but their diversity of formats and layouts makes that content hard to extract faithfully.

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Remembering Karen Dubinsky (1957-2026)

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Karen Dubinsky, Professor Emerita of History and Global Development Studies at Queen’s University, brilliant scholar and generous friend to so many, passed away on 4 September 2026 after a brief illness. Karen was a longtime supporter of—and contributor to—Active History. We mourn her loss, even as we celebrate her life. Below are three reflections from friends and colleagues of Karen. The first, by Susan Lord and Scott Rutherford, is reprinted with light editing from the website of the Department of Global Development Studies at Queen’s.

Susan Lord and Scott Rutherford

Karen was a colleague, a friend, an intellectual co-conspirator, a fellow traveler, a teacher and mentor for many at Queen’s and beyond. After graduating from Carleton University with her BA and MA, Karen completed her PhD in History at Queen’s in 1991. Karen had brief teaching stints at the University of Toronto, Carleton University and McMaster University before taking a tenure-track appointment in 1993 in the History Department at Queen’s University. She was jointly appointed to the Department of Global Development Studies in 2010. Her retirement from Queen’s in July 2025 did nothing to slow down her down, as until her untimely passing she was doing research for a new book, “Cuban Roots on Canadian Soil: A History of Cuban Musicians in Canada.”

She reinvented herself as a historian at every turn—her topics of study ranging from Canadian gender and feminist history to transnational adoption, from queer tourism to Cuba-Canada cultural relations. There were many other collaborations along the way: edited books on the global 1960s, transnational history, Cuban musicians, and global development studies in Canada. As her mentor and friend Dr. Roberta Hamilton said at Karen’s retirement event last year:

“In her research and teaching she has never stood still—and her shifting theoretical perspectives find their way lightly but cleverly to inform her substantive work. Which always makes her work so enlightening, surprising, and interesting. Take the first of her sole-authored books:  Improper Advances: Rape and Heterosexual Conflict in Ontario 1880-1929.  … Amazingly undated given the huge swings in theoretical approaches, Karen’s study intertwines sex/gender, race, sexuality, patriarchy, morality and the social construction not just of femininity but also of masculinity, until then seen as simply the norm. More than all this she shows what became her signature style in all her work: telling a good story.”

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Before “Lake America,” There Was “Lake Frontenac”

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By Scott Berthelette

On August 27, 2026, President of the United States Donald Trump signed an executive order directing the U.S. federal government to rename Lake Ontario “Lake America.” The order requires the U.S. Board on Geographic Names and federal agencies to use the new name on federal maps, databases, documents, and communications.

Immediately after Trump’s executive order, Canadian Prime Minister Mark Carney issued a statement explaining where the name Ontario comes from:

“The name Lake Ontario comes from the Wendat word Ontari’io, which, appropriately, means ‘the lake is beautiful, the lake is big.’”

The name, Carney emphasized, is more than four centuries old, predating both Canadian Confederation and the American Declaration of Independence. His conclusion was pointed: Canadians understand that “naming reality means calling it Lake Ontario – then, now and always.”

Figure 1: Canadian Prime Minister Mark Carney responds to the proposed renaming of Lake Ontario as “Lake America,” August 27, 2026. Screenshot of post on X (@MarkJCarney).

Ontario Premier Doug Ford’s response was considerably more combative and theatrical. On August 29, wearing a Team Canada soccer jersey, Ford unveiled an enormous 24-by-12-foot sign on the Lake Ontario shoreline near Hamilton:

“Lake Ontario. Now and Always.

Lac Ontario. Aujourd’hui et pour toujours.”

Figure 2: Ontario Premier Doug Ford (right) and Niagara West Member of Provincial Parliament Sam Oosterhoff unveil the “Lake Ontario: Now and Always” sign at Fifty Point Conservation Area in Grimsby, Ontario, August 29, 2026. Photograph by Keito Newman/The Canadian Press.

The $25,000 sign, elevated approximately six feet above the ground, makes the entire structure nearly eighteen feet high. “President Trump can call it whatever, but I can tell you the rest of the world will always call it Lake Ontario,” Ford said.

Figure 3: Lake Ontario displayed as “Lake America” on Google Maps for users in the United States.

The current controversy is not the first time that a government official has tried to give Lake Ontario a different name. In fact, the lake has carried several names over its long history. During the early and mid-seventeenth century, French writers and mapmakers variously referred to it as lac des Iroquois, lac Saint-Louis, lac Ontario,and Skanadario (“beautiful lake”). Of these competing names, Ontario ultimately endured, becoming the name not only of the lake but, in the late nineteenth century, of the province.[1] There was no single, universally accepted name for the lake during much of the seventeenth century.

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How Do We Talk about LLMs?

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By Jim Clifford

It is never fun to watch friends argue. Generative AI has created multiple fractures across our universities and the community of historians. The stakes are high and we are all dealing with the repercussions. I spent a few weeks in August rebuilding my second-year course for the third time. Two years ago, I swapped the traditional research essay for a StoryMap project, thinking that even if the students used GenAI, they’d still learn something making the map. But now, a student with Claude in Chrome could get the LLM to build the whole map without any real engagement. This year I’m trying a hybrid of contract grading and ownership. It was a lot of work and I still worry about what we’ve lost without a traditional essay.

The frustration and anger with the tech companies make it hard to hold a conversation. I’m worried about the future of higher education at the scale it has operated for the past half century. If machines are starting to displace recent graduates in the employment market, then the basic promise of working hard to earn a fulfilling and remunerative career is under threat. I understand the decision to simply refuse: not to engage with this technology and not to pay Anthropic or OpenAI a monthly subscription. I also understand why people are skeptical about any claims about the ability of LLMs. In 2024, OpenAI claimed PhD-level intelligence for models that couldn’t count the number of Rs in “strawberry.” Professors saw the regular failure of LLMs hallucinating historical arguments and citations. But a lot has changed in the past fifteen months, with the emergence of agentic systems, and then in mid-2026, with the launch of the Fable and Astra models. The problem is that both of these systems take investment to learn how to use and high-tier subscriptions. So, the vast majority of historians have not seen how they work.

My proposal here is to engage with those of us who are using the tools to understand where we stand in 2026 so we can collectively start to talk about what needs to change as we move into 2027 and 2028. I am not asking anyone to change their ethical stance, and I acknowledge there are large problems with the current build-out of data centres without enough regulations to mitigate environmental concerns. These issues are real, but I have decided, for myself, that critical engagement is how I can best understand the technology and think through its implications. The profession needs some of us working inside these tools; it does not need all of us to.

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Mexico’s Midcentury Nightlife

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Kevin M. Anzzolin

Since publishing my first book in 2024, I’ve turned my attention to gathering material for a second. Provisionally titled A View from the North: Lost Anglo-American Literature on Mexico from the Twentieth Century, I am exploring pulp novels, young adult adventure stories, tales of espionage, tourist guides, and yes, even postcards — in hopes of showing the extent to which the “Good Neighbor” cultural diplomacy of the twentieth century was, in fact, a genuinely intercultural endeavor between the United States and Latin America. Who were the small players — dime store novelists, postcard photographers, and nightclub owners — who got swept up in the large-scale international initiatives between North and South?

The Doris Z. Stone Latin American Library on the campus of Tulane University is an ideal place to examine this particular historical moment. The collection is replete with Mexico travel and tourism ephemera — magazines, hotel brochures, and highway maps — all of which, I believe, serve to better understand how our southern neighboring countries were constructed as attractive vacation destinations during the mid-twentieth century. Mexico especially loomed large in the imagination of US tourists; it was the period when, as historian Mauricio Tenorio shows, Mexico was understood as a “Brown Atlantis” — where revolutionary thought fermented and the Pan-American Highway promised access to exotic adventures. Everyone wanted to meet Frida Kahlo, everyone wanted to visit Teotihuacán, all while dreaming up global politics anew.

Two brochures found in the Mexican ephemera collection tell how Mexico feted itself for an international community.

Panoramic of the city center of Taxco de Alarcón, Guerrero, Mexico. Viewed from the Christ Monument looking southeast, via Wikimedia Commons.

First, is a trifold brochure from arguably Mexico’s most famous nightclub during the second half of the twentieth century — Cantarranas, located in the silver-mining town of Taxco, some two and a half hours outside of Mexico City. As the brochure details, the “Hacienda Cantarranas was built in 1538 by order of Hernán Cortés. The actual construction was done by his son Martin de Cortés.” The estate was used for silver processing. The locale, built with large stones on a hillside, became a nightclub in 1957 under the direction of entrepreneur Antonio “Tony” Reyes Ramírez. He would later become Taxco’s minister of tourism. Cantarranas was known worldwide for its revelry through the 60s and 70s before being acquired by the government of the Mexican state Guerrero in 1983, when it was converted into a culinary school and arts center. In 1992, it was turned over to the Universidad Nacional Autónoma de México as a school for foreign nationals. The hacienda was named “Cantarranas” — that is, literally “singing frogs” due to the loud chorus of frogs living in the area nearby. The brochure obviously plays upon this idea — even if not representative of the real spectacles of the club, the image is fetching.  

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An Anarchist Queen and Her Radiant Protégé: Marie Tiboldo and Emma Goldman in Toronto

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Cynthia Wright and Franca Iacovetta

In November 1926, Emma Goldman, then 57 and beginning a first period of exile in Toronto, enthused over a “radiant” young Italian woman seated at a welcoming dinner for Goldman organized by the city’s Jewish anarchist comrades.[1] Just over six months later, Goldman dubbed Marie Tiboldo “a terrible prude” who did “not feel anything deep enough, whether…for a man or ideas, to do anything worth while.” Tiboldo appears only fleetingly in Goldman’s estimated 200,000 letters, but her assessment of the young woman’s shortcomings aligns with Goldman’s pronounced tendency to go hot and cold with friends, comrades, and lovers. It’s not surprising, then, that Goldman also emphasized her continuing interest in “Marie,” writing that “I do not mean to give her up” and “I will certainly see much of her.”[2]

Marie Tiboldo (far right) at Bryn Mawr Summer School for Women Workers, 1929 (Julia Barnett Collection)

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Sapphic Sanctuaries: Navigating and Creating Lesbian Spaces in Fredericton, New Brunswick, 1969-1995

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Rosemarie Forsberg

In 1979, lesbians in Fredericton, New Brunswick, found community not in a dedicated gay bar or a visible neighbourhood, but on the dance floor of the Cosmopolitan Club. On Thursday nights, women quietly gathered with friends, effectively “queering” a predominantly heterosexual venue. In a city without lesbian bars, bookstores, or formal organizations, even a dance floor could become a place of belonging. From 1979 to 1995, lesbians in Fredericton actively built and sustained physical and imagined queer spaces that fostered community, activism, and belonging. Whether through dances, newsletters, phone lines, private homes, or women-only organizing, these spaces reveal how meaningful lesbian community could flourish beyond Canada’s metropolitan centres. In recovering these histories, Fredericton’s lesbian spaces emerge not as exceptions to Canadian queer history, but as evidence that community has always existed.

Queer history in Canada is often centred on the country’s largest urban centres: Toronto, Vancouver, and Montreal. Histories of queer activism have long focused on these cities, where visible institutions such as bars, bookstores, political organizations, and later gay villages left a stronger archival footprint. Much of the scholarship has similarly centred the experiences of urban gay men, tracing legal milestones such as decriminalization, police repression, and the rise of liberation movements. However, this focus overlooks the rich and varied experiences of lesbians in places like Fredericton, where queer history did not always take the form of visible institutions. Lesbian histories outside major cities challenge the assumption that queer communities depended on permanent spaces such as gay villages or commercial venues. Instead, many rural and small-city lesbians built networks through temporary gatherings, personal relationships, and community-based organizing.

As the provincial capital and home to two universities, Fredericton occupies a unique position within New Brunswick’s largely rural landscape. For many lesbians living in smaller towns, it became the nearest place to find others like themselves. Women travelled hours by car to attend dances, softball tournaments, and other events in the city, while local publications and phone networks helped sustain connections across long distances.

One of the clearest ways that lesbians created space in Fredericton was through community organizations. The founding of Fredericton Lesbians and Gays (FLAG) in 1979 gave lesbians access to meetings, dances, discussion groups, and a volunteer phone line. Even without a permanent headquarters, FLAG transformed temporary spaces such as university classrooms, church gymnasiums, and rented halls into meaningful queer spaces. However, the 1980s brought a notable decline in lesbian participation in mixed queer organizations like FLAG, shaped by broader tensions including lesbian separatist politics and ongoing experiences of misogyny and marginalization within male-dominated queer spaces. Across Canada, many lesbians responded to these tensions by creating women-only spaces that prioritized lesbian voices outside of male-dominated gay spaces. In Fredericton, this shift contributed to the founding of WOMYN in 1991, a lesbian-focused group that hosted and organized a variety of social activities, including dances, potlucks, barbecues, and softball games. By organizing regular social events in dedicated spaces, WOMYN created consistent opportunities for lesbians to build relationships and foster a sense of belonging.

Nightlife was another important way that lesbians in Fredericton carved out space, even without a dedicated lesbian bar. In the 1970s, lesbians gathered at the Cosmopolitan Club, a straight-owned venue on King Street that became a key nightlife spot for both gay men and lesbians, especially on Thursday nights. Because it was still risky for lesbians to dance openly with other women, gay men and lesbians often danced together in mixed pairs to appear as a heterosexual couple. By the 1980s and 1990s, this nightlife expanded through FLAG’s mixed dances and later WOMYN’s women-only dances, which offered safer and more intentional spaces for the lesbian community, visibility, and belonging.

An advertisement for Fredericton Lesbians and Gays (FLAG) from the first edition of Flag Mag, 1980. (Flag Mag No. 1, September 1980: 2)

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