Will Québec ever answer the ‘Immigrant Question’?

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By Theodore Aretakis

In the recent 2026 Québec elections, one topic once again captured the electorate’s attention: immigration. The Parti Québecois (PQ), which formed a minority government, presented twelve points on immigration during their campaign, notably setting permanent targets to roughly 35,000/year—a stark drop from last year’s figure of 60,000. PQ leader Paul St-Pierre Plamondon describes this initiative as important to the “protection of the French language, [the party’s] ability to deliver public services, and in terms of housing, and equal opportunities.”[1]

While this policy presents itself as a response to contemporary concerns, debates over immigration are nothing new for the francophone province. For decades, Québecois have grappled with the complexities of immigration in the context of their national project and perceived standing in Canada. However, this is not simply a two-sided debate. The Confédération des travailleurs catholiques du Québec (CTCC), whose statements and policies during the post-war period spanned both support and opposition, complicated the binary between “pro-” and “anti-” immigration. Examining the context and reasoning behind the contradictory sentiments of CTCC leadership and the rank-and-file provides insight and nuance into the development of the immigration debate in Québec.

The union’s periodical, Le Travail, offers a window into this complexity. In these pages, articles appealed to new arrivals by advertising their immigration support services and defending immigrants’ position in Québec society. At the same time, writers criticized government immigration policies and quotas, blaming them for high unemployment rates and the housing crisis.

CTCC president Gérard Picard.[2]
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Teaching Colonial Histories in Canada Through Relationship and Responsibility

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Chaa’winisaks and Pia Russell

Photograph from the shoreline of a rocky beach with an overhanging tree.
Beach at P’kols/PKOLS. P. Russell, 2026.

As the teaching of history in Canadian universities seeks to incorporate decolonization, anti-racism, and truth-telling, the work of how to realize these commitments in practice remains emergent. As a Nuu-chah-nulth and a White settler co-teaching team, we delivered a 12-week undergraduate history course at the University of Victoria (UVic) that focused on these commitments. In so doing, we treated relationship and responsibility as essential parts of the work of teaching history at a Canadian university. This post reflects on how relationship and responsibility shaped our pedagogy, describes what we learned in the process, and encourages others to consider how our experience might inform their own teaching. 

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Choosing to Stay

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This post is an introduction to the series Tomorrow’s Historians: A Reflection on the Graduate Student Experience 

By Katie Carson and Tiffany Chénier 

In a pervasive climate of anti-intellectualism and cuts to humanities programs, the decision to pursue a degree in history is an existential one. It is not uncommon for prospective students applying to PhD programs to receive “The Talk.” It typically begins by cautioning that the academic job market remains dire and that it is unlikely you will secure a tenure-track appointment upon completing a degree.  

These talks didn’t end once you received your acceptance letter. The malaise of an uncertain future hangs heavily over graduate students as they try to balance completing a degree with planning for an unpredictable postgrad career. 

The reality that studying history can be an isolating experience adds insult to injury. As a history graduate student, you’re likely to spend many solitary hours doing archival research, analyzing your sources, and drafting your chapters. You might also be the only student in your department studying a given sub-discipline, adding a layer of intellectual loneliness to the equation. Taken together with the grim job market, this isolation makes the decision to pursue a graduate degree increasingly daunting.

And yet, each year, new students embark on this journey.

Students at the University of New Brunswick, 1908.
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On the Useless History Degree

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By Sarah Kittilsen

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.
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Returning to Woodside: Changing Perspectives on a Historic House

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Shannon Stettner

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.

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Citation Needed: Anti-Equity, Diversity, and Inclusion Rhetoric from the Standing Committee on Science and Research

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Sara Wilmshurst

Colour photo of the Canadian Parliament's Centre Block and Library seen from a lower level and a distance.
Centre Block and Library of Parliament seen from Major’s Hill Park, Ottawa by Ymblanter. Creative Commons Attribution-Share Alike 4.0 International.

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.

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Alberta, I Love You, But You’re Bringing Me Down

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By Annabelle Penney

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 The Odyssey. 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.
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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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