Bibliographic record
Abstract
Until fairly recently the study of the borderlands of the United States has tended to concentrate on the southwestern frontier with Mexico. The noted historian Herbert Eugene Bolton (1870–1953) and his students led the way for others to follow in terms of research and the evolving conceptualization of the field. There was much to study. Here was a meeting place of two distinct languages and cultures. It had once been part of Mexico, but even after the United States entered the picture, plenty of excitement remained, including the Mexican Revolution (1910–1920), to attract the interest of historians. Much more recently, excellent scholarship has been produced on the northern borderlands. Perhaps the lag between the study of the two regions can be attributed to the perception that the northern borderlands were placid by comparison and thus a bit dull for study. With the exception of Quebec, the two nations spoke the same language and shared a heritage derived in large measure from Great Britain. Except for some saber rattling during the Civil War, the two nations managed to remain on peaceful terms after the War of 1812. The stream of Canadian migration west often veered south of the Great Lakes to avoid the formidable barrier of the Precambrian shield, and thus a generation of Canadians fit seamlessly into American culture before they or their children returned to settle the Canadian prairies. It is a neat pattern—too neat it turns out.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.001 |
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".