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Record W4411880703 · doi:10.1177/01634437251320831

Mediating the heatwave: Inside Karachi’s thermal ecology

2025· article· en· W4411880703 on OpenAlexaff
Ayesha Omer

Bibliographic record

VenueMedia Culture & Society · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsYork University
Fundersnot available
KeywordsEcologyGeographyBiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.268
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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