As a tour guide, I answer a lot of questions. Some are related to the tour itself, but many are not. Guests ask about my life just as much as, if not more than, they ask about the history of Montreal. I wasn’t surprised, then, when a woman turned toward me one summer morning and asked,
“So, what do you study?”
I’d answered this question enough times to perfect my response. Most guests were looking for small talk, so I kept it brief, including just enough detail to inspire confidence in my competence.
“I’m finishing a master’s degree in modern Canadian history this fall,” I said.
Guests usually offered a word of encouragement or nod of support in reply, or if they were chatty, an anecdote about a cousin’s husband’s daughter who also had a history degree. But not this woman.
“What are you ever going to do with that?”
Image 1: The author guiding a historical walking tour in Montreal.
Exterior of Woodside National Historic Site. Photograph by the author, December 2025.
Last December I visited Woodside National Historic Site, the boyhood home of Canada’s tenth Prime Minister, William Lyon Mackenzie King (WLMK). It was my first visit to the house since 1998 when I was a public history graduate student at the University of Waterloo. Back then, I wrote a paper on the portrayal of gender at Woodside. Decades later, while sorting through file boxes, I found the paper and wondered whether the interpretation had evolved in the intervening years. I don’t know if it has changed all that much, but my impression of it has.
In 1998, as part of my research, I accompanied a group of eight-year-olds on their school tour. While I got to witness their enthusiasm for the house, not surprisingly, the tour didn’t touch on my own interests. Instead, the emphasis was on portraying “Willie” as a “regular” kid. My assignment fell a few pages short, so I included an experiential analysis of my visit. It is an understatement to say that approach made my professor uncomfortable (he used the word “fretted” in his feedback). Nevertheless, I wrote about how walking through the house made me feel. I was a little disappointed that the house portrayed gender in the familiar ways we would expect of a Victorian house museum. I left wondering is that all there is? This was not a criticism of the staff or the story that was told. Rather, I was frustrated by what I perceived as the limitations of how gender could be conveyed in a Victorian household; 1998 me couldn’t relate.[i]
Prior to visiting in 2025, I contacted Woodside with an ambitious list of interpretive and operational questions. When I arrived, Holly took me on a tour of the house. What was planned as an hour-long visit turned into two. Because Holly knew my interests ahead of time, she pulled out specific artefacts for me to look at. For example, she found an etiquette book on the shelves of the library, showed me a telegram from WLMK to the South Waterloo Women’s Liberal Association, and shared a dance card from a July 15, 1889, party in the King’s parlour.
The recent report from the House of Commons Standing Committee on Science and Research (SRSR), “Impact of the Criteria for Awarding Federal Funding on Research Excellence in Canada,” delivers the Standing Committee’s recommendations for how postsecondary institutions and the Tri-Agency research funding bodies can support research excellence in Canada across all disciplines. As with all parliamentary committee reports, it includes a precis of the evidence gathered and notes where the full briefs and testimonials cited can be found online. The report was only presented to Parliament on September 21 and, at the time of writing, the government has not yet responded. It remains to be seen how this report will impact Tri-Agency policy.
The report is already worth discussing and critiquing, though, because it illustrates that Canada is not insulated from inflammatory and misleading rhetoric targeting equity, diversity, and inclusion (EDI) measures. The Trump White House has launched noisy and efficacious attacks on EDI in post-secondary education (and many other sectors of public life). Many Canadian academics are worried about our colleagues in the US but, as the SRSR report indicates, the call is also coming from inside the house. It is especially disappointing to see the committee bow to culture war pressures rather than weighing the quality of evidence.
I did not expect to cry on the way home from the Calgary airport. It had been over a year since my last homecoming, and recently I’d wondered if ‘home’ still fit Alberta. I’d forged newfound allegiances to Quebec, a province embroiled in its own separatist debates. While working on my thesis for some of the flight, I’d also watched Christopher Nolan’s TheOdyssey. Perhaps that informed my melodrama.
Beyond a certain political charge in the air, I didn’t know what to expect. Could Alberta still be hospitable to a committed Canadian? Since May, I’ve monitored Alberta’s political developments intently. Those 35 percent of Albertans who had polled in favour of separatism kept me up at night. Not out of reproach, but curiosity. The parallels between my own research on the provincial-federal squabbles between Pierre Trudeau and Peter Lougheed in the 1980s and the Alberta of today were evident.
The point is, I was disheartened by the state of the province, tired of being disappointed by the selfishness of our government, and couldn’t help but see a despicable cycle perpetuating itself. On the flight, I’d stumbled upon something curious in the Edmonton Journal. A meagre article about Grant Notley and a two-week unity tour he undertook in December of 1980. Amid a sea of Op-Eds and articles skewering the federal government over the National Energy Program, here was a story of hope: “Notley on road for unity.”
Front page of the Edmonton Journal, December 1, 1980.
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.
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.
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.
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.
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.
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.”
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.”
Figure1: 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.”
Figure2: 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.
Figure3: 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.
