Gwenn A. Miller . Kodiak Kreol: Communities of Empire in Early Russian America . Ithaca: Cornell University Press. 2010. Pp. xxi, 216. $55.00.
Bibliographic record
Abstract
Conceived as a case study of North American colonialism, this book uses the island of Kodiak, under Russian rule between 1784 and 1818, to explore gender, ethnic intermixing, and empire. Trained as a historian in early American history, Gwenn A. Miller also draws extensively on the booming literature on Russian imperial borderlands to illuminate what social interaction on this island off the coast of Alaska, where the cohabitation of Russian men with indigenous Alutiiq women resulted in mixed-race children whom colonial officials later called kreoly (usually rendered as creoles in English), can tell us about colonialism more broadly. Because it is a period of so few sources on the creoles, the author's choice of temporal frame seems surprising, but it is justified by her concern with origins, specifically the formation of the creoles as a distinct group. Recognizing the problem of source limitations (p. xi), Miller proposes to compensate through “careful interpretation and judicious inference” (p. xv). Inspired by the methodologies of Sylvia Van Kirk, Ann Laura Stoler, and Greg Dening and incorporating historiography on colonial interactions around the world, this book aims to interrogate “tense and tender ties,” “beach crossings,” and mutual dependence between Russians and indigenous people and to situate Russia's overseas colonial experience within the larger framework of North American colonial history.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 0.027 |
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".