What Do They Think of Us? American Perceptions of Québec Before, During and After Trump’s First Term in the White House
Bibliographic record
Abstract
Abstract This chapter argues that the election of Donald Trump in 2016 has prompted Americans to take a greater interest in Québec and become more skeptical about the benefits of Québec-U.S. relations, and that some of these trends persisted during Joe Biden’s presidency. Drawing on the work of Richard Fenno and his qualitative “soaking and poaking” approach, we conducted field surveys during the 2014, 2018 and 2022 U.S. midterm elections in ten New England and Midwestern states that the Québec government describes as part of its “historic strategic perimeter” to compare how Americans in these regions perceived “La Belle Province” before, during and after Trump’s first term in the White House, on issues such as trade and the economy, the border and immigration, and climate and the environment. We conducted semi-structured interviews with dozens of actors interested in Québec-U.S. relations: federal and local candidates for office, voters, journalists, lobbyists, trade unionists, campaign advisors and managers, staff members of party organizations. Presenting original qualitative data, our research method enables us to offer a complementary perspective to that of scholars who rely exclusively on survey data to try to “measure” how Americans and Canadians/Québecers perceive each other.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".