Preface and Acknowledgments
Bibliographic record
Abstract
Gathering Places presents an innovative collection of essays that spans a wide range of approaches and methods of Aboriginal and fur trade history in northwestern North America.Whether discussing dietary practices on the Plateau, trees as cultural and geographical markers in the trade, the meanings of totemic signatures, issues of representation in public history, or the writings of Aboriginal anthropologists and historians, the authors link archival, archaeological, material, oral, and ethnographic evidence to offer novel explorations that extend beyond earlier scholarship centred on the archive.They draw on Aboriginal perspectives, material forms of evidence, and personal approaches to history to illuminate cross-cultural encounters and challenge paradigms of history writing.The essays in this volume owe much to the scholarly example, leadership, mentoring, high standards, generosity, and encouragement that Jennifer S.H. Brown provided to the contributors during the course of her career.Many of us have shared either the privilege of being Jennifer's student or the experience of asking her for help only to realize her extraordinary generosity: our theses, papers, and book manuscripts have benefitted from her editorial eye and copious suggestions for further reading.These suggestions were often made while Jennifer and her husband, Wilson, fed us and put us up in their home for extended periods so that we could do research at the archives in Winnipeg.Jennifer's library has saved many of us from scholarly lapses, and her knowledge of theses, dissertations, and other unpublished work in progress has been crucial to developing conversations among budding scholars.Some of these scholars were influenced by her teaching at the University of Winnipeg, where she has for some time taught both undergraduate and graduate courses in Aboriginal history; others were graduate students whose theses she examined; some were postdoctoral fellows whom she supervised.This combination of knowledge and generosity led to the development of an important network of scholars that coalesced in the Centre for Rupert's Land Studies and its colloquia.Over time, and in various ways, Jennifer has fostered new ways of understanding and writing about the histories of the many peoples of Rupert's Land.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.194 | 0.112 |
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".