Bibliographic record
Abstract
The sleepy town of Emerson, Man. has been much in the news recently as the entry point to Canada for refugees fleeing Donald Trump’s America. But this is not the first time Emerson’s role as a border town has thrust it into the limelight.\nIn 1876, U.S. soldier Eugene Shinkle decided to cross into Canada at Emerson, where he proceeded to get very drunk and disorderly in a bar. Shinkle no doubt assumed he could blow off some steam beyond the reach of American military law. However, a U.S. army officer who was searching for deserters got wind of the incident and asked a friend of his, a lawyer in Emerson named Thomas Bevans, to seize Shinkle. The U.S. army officer and Bevans entered the bar and Bevans subdued Shinkle, who was bundled into a sleigh and soon found himself back on the U.S. side of the border at Fort Pembina where he would be disciplined. Upon returning to Emerson and finding there was a warrant for his arrest, Bevans himself slipped back across the U.S. border until the heat was off. And it soon was, because the provincial government dropped the charges against Bevans, aware that cross-border abduction of deserters and troublemakers was an accepted practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.076 | 0.019 |
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".