Blood, Water & Bathurst Street: Navigating an active relationship to land, place, and community through textiles.
Bibliographic record
Abstract
Blood, Water, & Bathurst Street is about navigating an active relationship to land, place, and community through textiles. This project began with exploring my family’s multi-generational history here in this place now known as Toronto, and the broader Jewish community that has grown here. Beyond blood relations, I have sought to establish further connection and understanding of/with the lands and waters that have shaped these territories. Many Indigenous Peoples have dwelled, gathered, and journeyed through these lands for millennia, yet their stories and ongoing presence have been largely erased from public memory here in the city. The Map, made of an 18-metre-long scroll of wool fabric, encompasses Bathurst Street and its geographic surroundings, from the current shore line of Niigani-Gichigami (Lake Ontario) up to Steeles Avenue (the City of Toronto’s northern boundary). It is unequal parts of my family tree, topographic exploration, historical survey, storybook, and material research. The exhibition Chapter One: A Map is Born, on from March 8th-12th, 2023, served as the Map’s introduction to the public, where folks were invited to contribute their own narratives and knowledge, expressed through a variety of materials.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".