Bibliographic record
Abstract
Making a vital contribution to our understanding of North American borderlands history through its examination of the northernmost stretches of the US-Canada border, Andrea Geiger highlights the role that the North Pacific borderlands played in the construction of race and citizenship on both sides of the international border from 1867, when the United States acquired Russia’s interests in Alaska, through the end of World War II. Imperial, national, provincial, territorial, reserve, and municipal borders worked together to create a dynamic legal landscape that both Indigenous and non-Indigenous people negotiated in myriad ways as they traversed these borderlands. Adventurers, prospectors, laborers, and settlers from Europe, Canada, the United States, Latin America, and Asia made and remade themselves as they crossed from one jurisdiction to another. Within this broader framework, Geiger pays particular attention to the ways in which Japanese migrants and the Indigenous people who had made this borderlands region their home for millennia—Tlingit, Haida, and Tsimshian among others—negotiated the web of intersecting boundaries that emerged over time, charting the ways in which they infused these reconfigured national, provincial, and territorial spaces with new meanings.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".