Cultural Mapping and the Digital Sphere
Bibliographic record
Abstract
“Notwithstanding their differing approaches—digital, archival, historical, iterative, critical, creative, reflective—the essays gathered here articulate new ways of seeing, investigating, and apprehending literature and culture.” – From the Preface This collection of essays enriches digital humanities research by examining various Canadian cultural works and the advances in technologies that facilitate these interdisciplinary collaborations. Fourteen essays—eleven in English and three in French—survey the helix of place and space. Contributors to Part I chart new archival and storytelling methodologies, while those in Part II venture forth to explore specific cultural and literary texts. Cultural Mapping and the Digital Sphere will serve as an indispensable road map for researchers and those interested in the digital humanities, women’s writing, and Canadian culture and literature. Foreword by Susan Brown and Mary-Jo Romaniuk. Contributors: Jeffery Antoniuk, Susan Brown, Constance Crompton, Ravit H. David, Patricia Demers, Shawn DeSouza-Coelho, Cecily Devereux, Teresa M. Dobson, Sandra Gabriele, Isobel Grundy, Andrea Hasenbank, Paul Hjartarson, Kathleen Kellett, Sasha Kovacs, Vanessa Lent, Margaret Mackey, Breanna Mroczek, Bethany Nowviskie, Ruth Panofsky, Mariana Paredes-Olea, Harvey Quamen, Jennifer Roberts-Smith, Omar Rodriguez-Arenas, Mary-Jo Romaniuk, Stan Ruecker, Lori Saint-Martin, Michelle Schwartz, Stéfan Sinclair, Mireille Mai Truong, Stéphanie Walsh Matthews, Heather Zwicker.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.016 | 0.047 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".