Settler Ecologies and the Future of Biodiversity: Insights from Laikipia, Kenya
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
Abstract This article examines the relationship between settler colonialism and biodiversity. Focusing on Laikipia, Kenya, we argue that the types of plant and animal species present in the landscape have been shaped by historical and present power relations and often support settler colonial projects. We introduce five modes of violent ecological transformation that have been used to prolong and advance structures of settler colonialism in Laikipia: eliminating undesirable species from landscapes; rewilding landscapes with species deemed more desirable; selectively repeopling nature to create seemingly inclusive wild spaces; rescuing species at risk of extinction to shore up moral support for settler ecologies; and extending the range of settler ecologies by scaling wild spaces. Through these modes of ecological transformation, ecological relations of use and value to settler colonialism live on while other(ed) ecological relations are suppressed or erased. As efforts to implement the post-2020 Global Biodiversity Framework (GBF) gain momentum, attention to settler ecologies is vital. Although there is no denying that radical action is needed to halt and reverse global biodiversity loss, there is a pressing need to question what types of nature will be preserved through the GBF and whose interests these natures will serve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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