Benjamin Hoy. <i>A Line of Blood and Dirt: Creating the Canada-United States Border across Indigenous Lands</i>.
Bibliographic record
Abstract
The global pandemic has put pay to any doubts anyone might have had about the potential power of borders to impact our lives in the twenty-first century. Canadian prime minister Justin Trudeau and US president Donald Trump might not have agreed upon much in March 2020, but they worked together to close their shared border to all nonessential travel as they scrambled to prevent the spread of the COVID-19 virus. When the Canada-US border reopened, it was more onerous and unpredictable for individuals to make the crossing. Travelers had to provide documentation of their vaccination status and evidence of a recent negative COVID-19 test. With the additional expense and hassle of a border crossing, who could blame potential travelers for deciding to stay home? Benjamin Hoy’s ambitious new book reveals that the anxiety experienced by twenty-first-century border crossers is something they share with their nineteenth- and early twentieth-century predecessors. Adopting a sophisticated perspective on federal border control efforts, A Line of Blood and Dirt convincingly argues that Canadian and US authorities discovered that a “border of discouragement” proved a more effective barrier to movement than a boundary of “debarment” (219). While federal agents on both sides of the Canada-US border measured their success by tabulating denied entries and totting up the value of seized goods, Hoy explains how these activities had a more powerful indirect influence on border crossings by spreading fear far into the interior of both countries. From the perspective of federal officials, the border worked best when it discouraged individuals from attempting the crossing in the first place.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".