More-Than-Human Infrastructural Violence and Infrastructural Justice: A Case Study of the Chad–Cameroon Pipeline Project
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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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.016 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".