Bridging National Borders in North America
Bibliographic record
Abstract
Despite a shared interest in using borders to explore the paradoxes of state-making and national histories, historians of the U.S.-Canada border region and those focused on the U.S.-Mexico borderlands have generally worked in isolation from one another. A timely and important addition to borderlands history, Bridging National Borders in North America initiates a conversation between scholars of the continent’s northern and southern borderlands. The historians in this collection examine borderlands events and phenomena from the mid-nineteenth century through the mid-twentieth. Some consider the U.S.-Canada border, others concentrate on the U.S.-Mexico border, and still others take both regions into account.The contributors engage topics such as how mixed-race groups living on the peripheries of national societies dealt with the creation of borders in the nineteenth century, how medical inspections and public-health knowledge came to be used to differentiate among bodies, and how practices designed to channel livestock and prevent cattle smuggling became the model for regulating the movement of narcotics and undocumented people. They explore the ways that U.S. immigration authorities mediated between the desires for unimpeded boundary-crossings for day laborers, tourists, casual visitors, and businessmen, and the restrictions imposed by measures such as the Chinese Exclusion Act of 1882 and the 1924 Immigration Act. Turning to the realm of culture, they analyze the history of tourist travel to Mexico from the United States and depictions of the borderlands in early-twentieth-century Hollywood movies. The concluding essay suggests that historians have obscured non-national forms of territoriality and community that preceded the creation of national borders and sometimes persisted afterwards. This collection signals new directions for continental dialogue about issues such as state-building, national expansion, territoriality, and migration.Contributors: Dominique Brégent-Heald, Catherine Cocks, Andrea Geiger, Miguel Ángel González Quiroga, Andrew R. Graybill, Michel Hogue, Benjamin H. Johnson, S. Deborah Kang, Carolyn Podruchny, Bethel Saler, Jennifer Seltz, Rachel St. John, Lissa WadewitzPublished in cooperation with the William P. Clements Center for Southwest Studies, Southern Methodist University.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".