Bibliographic record
Abstract
From the Editors June Scudeler tawow, welcome! Special sections and special issues are proving to be a vital way for Studies in American Indian Literatures to engage scholars and readers within our field, and to produce vibrant new knowledge about Indigenous writing. We’ve had dynamic special issues on Water (Fall/Winter 2018, guest edited by Christina Boyles and Hilary Wyss) and, earlier this year, on Indigenous Literatures from Canada (guest edited by Michelle Coupal, Aubrey Hanson, and Sarah Henzi). These issues have had enormous appeal for readers and have helped to build and reinforce community within Indigenous literary studies. Keep the proposals coming! In the issue at hand, we are pleased to present a special section devoted entirely to Deborah Miranda’s Bad Indians: A Tribal Memoir (2013). For nearly a decade, many of our colleagues have read, written about, and taught Miranda’s powerful, palimpsestic book, pondering what it tells us about California Mission history, colonial and personal archives, violence and intergenerational trauma, tribal genealogies, and of course, Native sovereignty and survivance. Guest editors Laura M. Furlan and Lydia M. Heberling have written and collected a thought-provoking collection of essays that walk us through these themes and more . . . including a piece by Deborah Miranda herself. In addition to the special section, we have two other essays on canonical writers. Jane Im reads Louise Erdrich’s The Beet Queen through the lens of traditional Ojibwe plant knowledge, and Cristina Stanciu advances our understanding of both the so-called “Red Progressive” era and of Indigenous print culture with some close readings of Carlos Montezuma’s early twentieth-century newsletter, Wassaja. The span of essays represented here—from elevating one of our most innovative contemporary Indigenous authors to revisiting a more well [End Page vii] known Native American Renaissance novel to exhuming new knowledge about the more distant history of Indigenous periodical literature—represents the kind of diversity and imagination we have come to expect from SAIL contributors, and to which we look forward to seeing more. [End Page viii] June Scudeler Siobhan Senier Copyright © 2021 University of Nebraska Press
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.554 | 0.526 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".