Roads, Lines, and Boundary Objects: A Critical Cartographic Look at the Development of the Serengeti Highway
Bibliographic record
Abstract
In 2010, the then President of Tanzania, Jakaya Kikwete, announced an economic development plan to build a highway that spanned northern Tanzania. The map of the plan revealed a transportation network that traversed the Greater Serengeti–Mara ecosystem and bisected the iconic Serengeti National Park. Given the importance of the Serengeti–Mara ecosystem to conservation and savannah ecology, not to mention international tourism, the plan immediately came under worldwide scrutiny. The Tanzanian state and the Kikwete administration were lambasted, ultimately fixing the highway project in the form of polylines on maps rather than an infrastructure project on the ground. As such, the mutability of lines on maps give the highway project the flexibility to transcend conversations across institutions that span the conservation–development spectrum with limited interrogation. This article addresses the circulation of maps representing the failed highway project through various epistemic communities. My purpose is neither to advocate the development of the Serengeti Highway nor to criticize it. Rather, I use examples of Serengeti Highway maps to discuss the way cartography travels through these conversations, reflect on the international conversation surrounding the Serengeti Highway project, and explore the question of who can (and cannot) draw lines in the contested spaces of East African rangelands.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.025 | 0.034 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".