Bibliographic record
Abstract
In the summer of 2004 I began filming scenes for what I thought was going to be a lyrical and quirky look at weather stories and weather lore across Canada. Climate change, or a section I called 'The Politics of Weather', was obviously going to be included but at that moment I thought it would be confined to one section, interspersed with weather proverbs or amusing bon mots from amateur weather observers. Like most people, I had a vague idea of carbon cycles and a dim appreciation of the complexities of Kyoto and emissions reductions. I was wary of apocalyptic scenarios but susceptible to low-level dread at the steadfast accumulation of international weather disasters, not to mention the increasing summer temperatures and smog days in my own city of Toronto. I left lights on, I used my dryer, I drove to work (I love my car). But the climate crisis is an issue that gets under your skin; ask any climate activist. That's because its dimensions are so all-encompassing and the task of addressing the issue is so urgent. It's a geopolitical issue as much as it is a local issue. It connects to the immediate materiality of our individual bodies, as much as it implicates energy regimes, models of development, how we organize cities, suburbs and transportation systems, public utilities and private corporations. It crosses issues of social justice in the global south and the crisis of democracy just about everywhere, and it puts the future on the agenda for all of us, in a way, as Andrew Ross suggests, that has not been seen since the mass socialist movements of the 1930s.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".