Bibliographic record
Abstract
Alberta writing has a long tradition. Beginning with the pictographs of Writing-on-Stone, followed by Euro-Canadian exploration texts, the post-treaty writing of the agrarian colonization period, and into the present era, Alberta writing has come to be seen as a distinct literature. In this volume Melnyk and Coates continue the project of scholarly analysis of Alberta literature that they began with Wild Words: Essays on Alberta Literature (2009). They argue that the essays in their new book confirm that Alberta's literary identity is historically contingent with a diverse, changing content, that makes its definition a work-in-progress. The essays in this volume provide contemporary perspectives on major figures in poetry and fiction, such as Robert Kroetsch, Sheila Watson, Alice Major, and Fred Stenson. Other essays bring to light relatively unknown figures such as the Serbian Canadian writer David Albahari and the pioneer clergyman Nestor Dmytrow. Writing Alberta: Building on a Literary Identity offers a detailed discussion of contemporary Indigenous writers, an overview of Alberta historiography of the past century, and the fascinating autobiographical reflections of the novelist Katherine Govier on her literary career and its Alberta influences. This Collection demonstrates that Alberta writers, especially in the contemporary period, are not afraid to uncover, re-think, and re-imagine parts of Alberta history, thereby exposing what had been lain to rest as an unfinished business needing serious re-consideration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.129 | 0.035 |
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".