INFRARED IMAGINATIONS AND CLOUD-TRUTH: CLASSIFYING WEATHER IN THE SATELLITE AGE
Bibliographic record
Abstract
Like sex, weather is a social creation. Indeed, we talk about the weather more often than we talk about sex, and, as for most natural processes, society has created structures of meaning that define atmospheric change in human terms. These meanings evolve as technology and scientific thought progress, creating new ways of experiencing the weather and of literally seeing the sky. In modern, enlightened times, the weather has become subject to thorough classification, formulation and social regulation via new techniques of atmospheric observation and scientific processing. As the technocultural eye sees the atmosphere differently, ideas about what the weather means also change. Berland notes that the “most brazenly unruled of all the cyclical processes of ‘Nature’ turns out to be shaped differently by our different imaginations, and now haunts our material symbolic expressions through inversion, distortion, condensation,and absence” (1999). The endless sky becomes an endless seriesof digitized patterns and formulas, whose earthly results nonetheless connect to the most visceral and emotional centres of human consciousness.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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 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".