Bibliographic record
Abstract
Echoing the pandemic-era phrase “shelter in place,” and extending beyond it, this collection examines how writing can create, illuminate, and complicate ideas about dwelling, belonging, or finding safe harbour. Through an engaging blend of academic essays and creative nonfiction, contributors interrogate the connections between the concepts of shelter and text, centering questions of care, disability, and housing inequality. How does the physical infrastructure of the city interact with literary form and how do stories bring attention to our built environments? Did the experience of lockdown (re)shape our interiorities, imaginations, and reading habits? Can Indigenous and decolonial approaches to land and storytelling and an inclusive practice of shelter-making through narrative enable a more sustainable future? While many of the works and writers discussed in the volume are Canadian, the scope extends beyond national borders to create a transnational dialogue on diverse and non-traditional approaches to topics of land, space, and shelter. Shelter in Text will appeal to literary scholars, particularly those working in the fields of Canadian literature, Indigenous studies, contemporary literature, ecocriticism, gothic fiction, Queer studies, feminist studies, disability studies, translation, and literary theory. Contributors: Kelly Baron, Billy-Ray Belcourt, Myra Bloom, David Chariandy, Lily Cho, Sophie Feng, Ryan Fitzpatrick, Kristi Leora Gansworth, Sarah Gordon, Shannon Griffin-Merth, Anna Guttman, Heather Jessup, Andrew David King, Caroline Lavoie, Jennifer Lawn, Jessi MacEachern, Kayla Penteliuk, Anil Pradhan, Geneviève Robichaud, Kasia Van Schaik, Holly Vestad, Erin Wunker, and Robert Zacharias.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.023 |
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".