Bibliographic record
Abstract
Drawing from sound studies, energy humanities, and anthropology, this essay identifies a critical gap in the academic recognition of “petrophonics”—sonic and vibrational byproducts of fossil fuel dependency that pervade contemporary soundscapes—within sound and soundscape studies as well as the environmental and energy humanities, where such phenomena are often dismissed as “white noise” or background ambience. Through theoretical analysis and empirical observation, we attempt to define petrophonics as both an object of study and a framework for critical engagement. Focusing on traffic noise—the most accessible example of petrophonics—as a cultural and material phenomenon rather than a mere auditory background allows us to explain and propose a speculative definition of petrophonics, characterized by its materialist grounding in fossil fuel infrastructures and its capacity to exist independently of audibility. This essay concludes by emphasizing the political, temporal, and decolonial dimensions of petrophonics, advocating for an ethico-affective approach that foregrounds the relational and infrastructural realities of petromodernity. This framework invites scholars and practitioners to re-attune to the pervasive yet overlooked sounds of fossil fuel dependency and imagine alternative, post-petrocultural phonic worlds.
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 | 0.001 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".