books as bodies or bodies as maps
Bibliographic record
Abstract
Since my move to so-called Canada in 2015, I have reconsidered the way in which the places I have dwelled are bound to the temporal materialities around me. Through my transits in Mexico City, Oaxaca, Corner Brook, St. John’s, and Vancouver, I have come to conceive my body as my primary tool of creation, a site for translation among different systems of value and belief. In books as bodies or bodies as maps I explore this entanglement of embodied relations by repurposing the book as a body and the body as a map. Repurposing Sonja Boon’s biographical novel What the Oceans Remember as a pinhole book/camera, I took photos along the Vancouver shoreline as a gesture of acknowledgment to the water as the common feature for untold human and non-human stories. Resisting containment, I present the documentation of the places I have collected on walks over the course of 2020-21 in postcards, booklets, a map, and a three-channel video. This is the first iteration of an unfolding project in which I constellate and document the places I have transited through (sometimes as a “temporary resident”, sometimes as a citizen) in an attempt to re-see the book as a re-writing device that ultimately affords readers the agency to drift.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.015 |
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".