“Foreigners in Town”: Leisure, Consumption, and Cosmopolitanism in Late-Nineteenth and Early-Twentieth Century Tillsonburg, Ontario1
Bibliographic record
Abstract
At the turn of the twentieth century, the small town of Tillsonburg, Ontario contained a population that was over eighty-six percent Anglo-Celtic in origin. Increasingly, however, local leisure activities began to incorporate “ethnic” food, dress, and mannerisms so that citizens could momentarily become “foreigners” and “consume” knowledge of other nations and peoples. Two of the more notable venues where participants acted out racialized identities—the “Garden Party of the Nations” and the Ladies’ Travel Club—showcase desires to consume markers of foreignness while concurrently displaying an acceptance of these cultures. Using Tillsonburg as a case study, this article examines how engaging with the “foreign” in patterns of leisure and consumption was a way for citizens (and women in particular) to convey an air of cosmopolitanism and cultural refinement in the face of critiques that small towns were insular and unsophisticated. Their cultural appropriations, however, represent grossly distorted understandings of races, ethnicities, and cultures that existed outside their Euro-Canadian one. Though efforts by Tillsonburg’s populace to appear more cosmopolitan were grounded in a desire to expand understandings of “the foreign,” this occurred only on their own terms and in controlled spaces.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".