Bibliographic record
Abstract
When the trees came down no one knew how / to interpret the light. homeless / it bounces off glass surfaces / pierces the wandering eye– These poems walk streets and take snapshots of the impact financialization of our homes has on our sense of community and belonging. Meandering through physical and philosophical materials – cement, memory, water, narrative, history, sand, light, concrete, and others’ voices – Daniela Elza documents this urgent moment. The reader winds through fragments amidst urban fragmentation. A sequence of triptych poems hearkens to silos, skyscrapers, and streets. Readers here have a choice: they can read across the page or down. She channels Syrian architect Marwa Al-Sabouni, who says, “The fabric of our cities is reflected in the fabric of our souls.” SCAR/CITY emerges from the Vancouver context to take on global issues of predatory finance and a market that mines homes for profit. It steps outside of binary conversations in favour of poetic reflection and interrogates a system that results in perceptible depravity and scarcity, which leaves us homeless, metaphorically and literally. French philosopher Gaston Bachelard says, “The space we love is unwilling to remain permanently enclosed … Space calls for action, and before action, the imagination is at work.” Amidst negotiations and advocacy in the fight for security of tenure and lease renewal, SCAR/CITY is a poetic call to action.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.512 | 0.304 |
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".