Canadian Journal of Environmental Education, 13 (2), 200830 Finding Home: A Walk, a Meditation, a Memoir, a Collage
Bibliographic record
Abstract
I unabashedly love Toronto, where I’ve lived for more than three decades. Two of those I’ve resided in this 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 my living and working sites with those institutions and my other haunts. What thrills me most about this city? Its diverse peoples, half of whom were born outside Canada.1 Its neigh-bourhoods. Its walkability. Its ravines. Leaving OCAD at the end of an afternoon’s teaching, I loved to stroll through the crowds, images, noises and smells of adjacent downtown Chinatown, enjoying the hurly-burly along Dundas Street West, the shapes and colours of the once-unfamiliar fruits and vegetables of the street-side displays, the streetcar’s clang of impatience as it noses through the congested traffic, the sweet, dense smells wafting from the herbalists ’ open doorways. I’m also enamoured of its counterpoint: the hipster urban gloss a few blocks south and west along Queen Street (close to my studio), its trendy storefronts filled with
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.145 | 0.013 |
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".