ENVIRONMENTAL FIELD COURSE: TORONTO’S URBAN METABOLISM COURSE SYLLABUS FALL TERM 2014
Bibliographic record
Abstract
“Imagine, for example, standing on the corner of London’s Piccadilly Circus, and consider the socio-environmental metabolic relations that come together in this global-local place. Smells, tastes, and bodies from all nooks and crannies of the world are floating by, consumed, displayed, narrated, visualised, and transformed. The Rainforest shop and restaurant play to the tune of eco-sensitive shopping and the multibillion-pound eco-industry while competing with McDonalds burgers and Dunkin ’ Donuts; the sounds of world music vibrate from Tower Records; and people, spices, clothes, foodstuffs and materials from all over the world whirl by. The neon lights are fed by energy coming from nuclear power plants and coal- or gas-fired electricity generators. The cars burning fuels from distant oil deposits and pumping CO2 into the air, affecting people, forests, climates and geopolitical conditions all around the globe, further complete the global geographic mappings and traces that flow though the urban and „produce ‟ London as a palimpsest of densely layered bodily, local, national and global – but depressingly uneven geographically – socio-ecological processes. This intermingling of things material, social and symbolic combines to produce a particular socio-environmental milieu that welds nature, society and the city together in a deeply heterogeneous, conflicting and often disturbing whole.”
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.643 | 0.363 |
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".