More-Than-Human Infrastructural Violence and Infrastructural Justice: A Case Study of the Chad–Cameroon Pipeline Project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As a new wave of infrastructure expansion takes place globally, there has been a parallel turn to infrastructure in geographical research. This article responds to recent calls within this research for less human-centered engagement with the infrastructure turn. More specifically, this article aims to destablize anthropocentric discussions about infrastructural violence and infrastructural justice. Using the Chad–Cameroon Pipeline Project as a case study, we advance two main points. First, we show that infrastructural violence is not solely directed at humans. Rather, all agents, objects, and conditions—from humans to fish to carbon sequestration—entangled in webs of relations within zones of infrastructural expansion risk being subjected to violence when new and existing infrastructures meet. To illustrate this point, we detail two examples of competitions between new and existing infrastructures along the Chad–Cameroon Pipeline route, which together reveal the various forms of violence experienced by the more-than-human world when new infrastructural arrangements are layered on top of already existing ones. Second, we advance debates on infrastructural justice by adopting a more-than-human perspective in our conceptualization of this term. Recent writing on infrastructural justice has reflected on efforts to repair and rebuild infrastructures to produce more just futures (Sheller 2018 Sheller, M. 2018. Mobility justice: The politics of movement in an age of extremes. New York: Verso. [Google Scholar]). Drawing on the observations and reflections of our fieldwork along the Chad–Cameroon Pipeline route, we argue that just infrastructure projects must not only be inclusive of marginalized human and nonhuman populations but they must also avoid interfering with the infrastructural work done by nature to sustain the more-than-human world.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it