Bibliographic record
Abstract
The author loves Toronto, where she has lived for more than three decades. Two of those she has resided in an apartment on the west side of town--close to the University of Toronto and the Ontario College of Art and Design (OCAD), to the parks and pathways that connect her living and working sites with those institutions and her other haunts. What thrills her most about this city? Its diverse peoples, half of whom were born outside Canada. Its neighbourhoods. Its walkability. Its ravines. When a person loves something as much as she does Toronto, then he or she makes artwork about it. In her practice, art making requires a devoted and voracious accumulation of ideas and options, followed by ruthlessly selective editing, a kind of delicate dissection--this paradoxical form of engagement somehow both bonds a person to his or her subject and holds it at arm's length. And so motivated by love, curiosity and a kind of anticipatory nostalgia the author explains in this article, she created "Finding Home," a body of interwoven visual art and text about her home neighbourhood around Bathurst and St. Clair. This article reflects on the making of "Finding Home" and includes portions of it. (Contains 22 notes.)
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".