One size fits all: How the “Ethiopian Highlands” made Bale Mountains National Park inscrutable
Bibliographic record
Abstract
The categories we use to make sense of a place are never neutral. Scientific classifications can maintain ignorance about some aspects of a landscape, even as they create knowledge about others. This article considers this in the context of Ethiopia's Bale Mountains National Park, a landscape whose hydrologic and socio-cultural characteristics have been made inscrutable through the convergence of imperial legacies, processes of knowledge production, and complex biophysical properties. We use the example to conduct a genealogy of the notion of the "Ethiopian Highlands" and its associated metaphors, tracing the political-economic, biophysical, and epistemic factors by which this category came into use, and how these intersected to maintain a particular yet partial vision of the region. By critically analyzing bibliometric data, historical sources, and chains of reasoning in the scientific literature, we show how a small group of foreign experts erroneously conflated the landscapes, peoples, and environmental concerns of one area with those of another. Together these forces reify imperial gazes, perpetuate degraded wilderness narratives, and overlook significant geologic, (paleo)climatic, sociocultural, and land use differences. The result is a simplistic understanding of a distinct hydrosocial landscape, the perpetuation of conflict and resentment, and poorer conservation outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".