<b>Infrastructural Brutalism: Art and the Necropolitics of Infrastructure</b>. <i>By Michael Truscello</i>
Bibliographic record
Abstract
Infrastructural Brutalism: Art and the Necropolitics of Infrastructure is certainly not a book to which those seeking a touch of optimism about our future will turn to; the title leaves no ambiguity.It is, however, a must-read for anyone interested in understanding infrastructures and the baneful effects of industrial capitalism on our planet.In taking stock of the infrastructural turn in social sciences and humanities, Truscello adopts an unequivocal critical stance to interrogate the necropolitics of infrastructures through the lens of artistic media.Starting from the premise that infrastructures such as power plants, dams, roads, and trains are visible yet plainly neglected in a wide range of artistic texts spanning from novels, films, and photographs, to television shows, he reveals how artistic media can tell us more about infrastructures than one might think.The core of his critique of infrastructures studies, and hence, his main and crucial contribution, is that although art is sometimes construed as a "mode of infrastructural visibility" (p.28), not enough attention has been paid to how art already presents a "substantial visibility of infrastructures in artistic texts, both contemporary and historical" (p.29).For instance, the failure of the "road movie" genre to be analyzed for its "visible material" infrastructure-despite the infrastructure literally constituting the title of the genre-is symptomatic of how even when they are visible, infrastructures are "made invisible," muted, or disregarded.Taking aim at what he sees as a reductive misinterpretation of "Susan Leigh Star's declaration that infrastructure is 'by definition' invisible" (p.29), Truscello makes the case for conceiving of "infrastructures as a complex assemblage of cultures, human actors, laws, economic imperatives, ecological webs, construction materials, and institutional, geographical, and
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".