Bibliographic record
Abstract
Digital Memory Agents in Canada explores memory performances and representations with different cultural and spatial relationships to Canada. It moves from discourses on place to focus on the digital or virtual space and on how certain cultures, subjectivities, or positionalities use digital media to document or represent their recollections. Embracing interdisciplinary approaches, the contributors investigate how digital media, like memories, can transcend space and time to impact individuals and communities. Chapters examine memorialization, documentation, and online activism; aesthetic productions and counter-productions of identity in literature, film, and beyond; queer and feminist archiving and consciousness-raising; and Indigenous, Métis, and Black narratives of resistance. These are narratives and research models that disrupt Canadian, hegemonic, colonial, white-centric, and patriarchal beliefs. Digital Memory Agents in Canada will be of interest to scholars and students specializing in memory studies, digital humanities, film and media studies, and cultural studies. Contributors: Jim Clifford, Matthew Cormier, Erika Dyck, Craig Harkema, Caroline Hodes, Russell J. A. Kilbourn, Jordan B. Kinder, Anna Kozak, Braidon Schaufert, Amanda Spallacci, Matthew Tétreault, Uchechukwu Peter Umezurike, Stephen Webb
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".