Bibliographic record
Abstract
In June 2015, amidst soaring temperatures, severe electricity, and water shortages, hundreds collapsed on the streets in Karachi. Emergency wards, morgues, and graveyards became full. Drawing on environmental media studies, infrastructure studies, and urban studies, the 2015 Karachi heatwave created, what I call, “a thermal ecology” that encapsulates shifting material, geophysical, cultural phenomena that comprise life inside a heatwave. I demonstrate that the thermal ecology reconsitutes mediation, as relationships between media forms, systems, and cultures reorganize in response to ubiquitous heat. I examine three instances of such thermal mediation: first, a media assemblage of citizen-led media activism disseminated through public and corporate media platforms; second, a data computation-driven urban governance plan to tackle future heatwaves; and, third, a community-based visual media art project, Of Struggle , which documents and intervenes against the violent loss of human, nonhuman life from urban development. While recent scholarship attends to the tremendous heat expended by digital media, this paper examines the thermal ecology as it conditions forms of mediation, and the social and ecological lifeworlds within which these are embedded. It argues that the thermal ecology – its media actions, governance, imaginaries – need urgent examination as excessive, violent heat is an ongoing condition and marked future for all.
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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 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".