Bibliographic record
Abstract
How do we breathe in/ breathe in disaster? Artist Christina Battle’s Forecast series (2020—ongoing) explores what it means to breathe in climate disaster, using prompts and observations to encourage its participants to sense and anticipate the drastically changing weather around them. Critiquing environmental racism, Battle’s work highlights the “unequal distribution of air pollutants,” activating Forecast with communities in Newfoundland, Toronto, and Edmonton in works like the air we breathe (2022) and Learning the Signals/Change is Coming (2022/2023). This exhibition review of Forecast presented at Gallery 1C03 (Treaty 1 Territory) highlights the significance of community-oriented, intimate actions of breathing and observation to grasp how environmental catastrophes are felt differently across geographies. This essay is rooted in the author’s ongoing conversations with Battle, and takes cues from Christina Sharpe’s idea of weather, Kristin Simmons’ notion of settler atmospherics and adrienne marée brown’s principles of emergent strategy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".