People from Everywhere: Metis Identity, Kinship and Mobility 1600s-1800s
Bibliographic record
Abstract
ABSTRACT PEOPLE FROM EVERYWHERE: METIS IDENTITY, KINSHIP AND MOBILITY, 1600s-1800s by Mark Langenfeld The University of Wisconsin-Milwaukee, 2021Under the Supervision of Professor Margaret Noodin People from Everywhere: Metis Identity, Kinship and Mobility, 1600s-1800s, is a discussion of how the Metis people of the American southern Great Lakes region in Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Ohio and Wisconsin made individual and familial choices about ethnic identification from the seventeenth to the nineteenth centuries that enabled them to survive colonization in their homeland. I argue that Metis people maintained, through kinship networks, a private identity as a collective, distinct group of indigenous people and a private sense of individual pride in their mixed ancestry, even as they performed acts of assimilation to white or Indian communities and moved through British, French, Anglo-American, Metis and Indian communities in the American southern Great Lakes regions. Their own self-identity was synonymous with kinship and a kinship system flexible enough to incorporate Indians and non-Indians of different backgrounds. They traversed communities of their Native relatives, Metis communities such as Chicago, Milwaukee, Mackinac, and Prairie du Chien, and the communities of the white British, French, or Americans who arrived and transformed the area by settlement. American Metis experienced different types of colonization differently while the United States was forming and the American government debated Metis status. Despite their political marginalization and apparent outward assimilation, the American Metis maintained a continuous presence in the U.S.A. and are still present today. This longitudinal micro-history explores the identity choices of American Metis families, including their social and geographic mobility and shifting ties to kinship across time and space and what local, national and international forces guided and shaped their choices. This micro-history is based on various primary sources, including census records, church records, memoirs and correspondence of the Metis themselves, and official fur-trade documents, as well as various methodologies, including American Indian Studies theory, feminist theory, and historical memory theory.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".