Bibliographic record
Abstract
A comprehensive, up-to-date overview of the work of one of the foremost Native North American writers and his reception and influence. Thomas King is one of North America's foremost Native writers, best known for his novels, including Green Grass, Running Water , for the DreadfulWater mysteries, and for collections of short stories such as One Good Story, That One and A Short History of Indians in Canada. But King is also a poet, a literary and cultural critic, and a noted filmmaker, photographer, and scriptwriter and performer for radio. His career and oeuvre have been validated by literary awards and by the inclusion of his writing in college and university curricula. Critical responses to King's work have been abundant, yet most of this criticism consists of journal articles, and to date only one book-length study of his work exists. Thomas King: Works and Impact fills this gap by providing an up-to-date, comprehensive overview of all major aspects of King's oeuvre as well as its reception and influence. It brings together expert scholars to discuss King's role in and impact on Native literature and to offer in-depth analyses of his multifaceted body of work. The volume will be of interest to students and scholars of literature,English, and Native American studies, and to King aficionados. Contributors: Jesse Rae Archibald-Barber, Julia Breitbach, Stuart Christie, James H. Cox, Marta Dvorak, Floyd Favel, Kathleen Flaherty, Aloys Fleischmann, MarleneGoldman, Eva Gruber, Helen Hoy, Renée Hulan and Linda Warley, Carter Meland, Reingard M. Nischik, Robin Ridington, Suzanne Rintoul, Katja Sarkowsky, Blanca Schorcht, Mark Shackleton, Martin Kuester and Marco Ulm, Doris Wolf. Eva Gruber is Assistant Professor in the Department of American Studies at the University of Konstanz, Germany.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.283 | 0.165 |
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".