The Routledge Introduction to English Canadian Literature and Digital Humanities
Bibliographic record
Abstract
The Routledge Introduction to English Canadian Literature and Digital Humanities is a guide to the concepts and theories at the intersection of Canadian literary studies and digital humanities (DH). Equal parts theoretical and practical, it focuses on debates that overlap the two domains. This book historicizes the connections between the two by surveying the history of DH in Canada, the tradition of Canadian writers engaging with technology, and DH analyses of Canadian literature. It also situates both CanLit and DH with respect to contemporary concerns about alterity, and it demonstrates how digital technologies allow writers and scholars to intervene in them. This book complements its theoretical discussions with a practical introduction to DH methods. Using Canadian literary texts and examples from projects at the intersection of CanLit and DH, it introduces key DH approaches to novice readers. Topics covered include data collection, data management, and textual analysis, as well as essential DH tools and the Python programming language. A concluding case study guides readers interested in applying the ideas presented throughout.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.022 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.102 | 0.032 |
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".