Bibliographic record
Abstract
sylvia van kirk's analysis of interracial marriage and mixed-race peoples in the western Canadian fur trade has influenced a generation of scholars working on Native women's history, the fur trade, Métis communities, and post-colonial history in Canada and the United States.But Sylvia's work has also reached beyond the borders of North America, shaping the scholarship and approaches of those working on the history of interracial marriage, gender, and colonialism in other former frontier societies like Australia and New Zealand.Southern New Zealand has a distinctive history of hybridity where male newcomers entered into interracial relationships, contributing to the development of a hybrid population that was welcomed and celebrated by officials and Aboriginal peoples.But this history of intermixing is not as well-known as the social worlds and societies created out of the North American fur trade.I explore this social world, taking Van Kirk's scholarship and methodology as a point of reference and extending it to the resource economies and frontier space of southern New Zealand while inviting connections with the histories of gender and colonialism in western Canada. 1 Southern New Zealand refers to what is known as Otago, Southland, and Stewart Island today, and is the tribal territory of Ngāi Tahu (Figure 1).Fostered by the arrival of the shore-whaling industry, Ngāi Tahu encountered newcomers on an extensive and sustained scale in this region from the 1820s.Unlike
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".