The Politics of Conservation Aid: The Development State and ‘Saving the Mau’
Bibliographic record
Abstract
The initiative to ‘Save the Mau’ brought various conservation aid actors to the Mau Forest, both governmental and non-governmental. The initiative can be understood in the context of a global rise in green governmentality, which included various international environmental agreements, including the 1992 Convention on Biological Diversity (CBD), the 1973 Convention on the International Trade in Endangered Species of Wild Flora and Fauna (CITES), the 1979 Convention on the Conservation of Migratory Species of Wild Animals (CMS), the 1994 Convention to Combat Desertification (CCD), the 1992 Framework Convention on Climate Change (UNFCCC), Agenda 21, the 1994 International Tropical Timber Agreement (ITTA), the 1997 Kyoto Protocol, the 1987 Montreal Protocol on Substances that Deplete the Ozone Layer, the 1971 Ramsar Convention on Wetlands of International Importance, alongside many others (Bongers & Tennigkeit, 2010; Johnson, 1976; Meyer et al., 1997; Okidi, 2008; Okidi et al., 2008). Conservation aid to Kenya progressively increased, which bore significant conservation efforts, including for the Mau Forest. Some of the conservation activities implemented were directly related to the Mau Forest Rehabilitation Programme. While NGOs had previously engaged with forest-dwelling and adjacent communities, these interactions were primarily focused on land rather than environmental issues. With the branding of the ‘Mau crisis’ as an environmental crisis, the politics of conservation aid arrived in the Mau Forest. The politicised environment: Environmental conservation and management concepts and practice In Kenya, forest management and conservation are embedded in and influenced by the broader environmental conservation, land use, and land-management history. Contemporary forest conservation policies and practices are a legacy of general environmental management policies and should be understood in their historical context. The colonial legacy in conservation narratives and practices During the colonial period, environmental stress was explained by a ‘single story’, a kind of ‘master narrative’. This colonial legacy defined the trajectory of environmental policy and practice in Kenya, which influenced the multiple attempts and failed approaches to conserving the country's spectacular environments. Institutionalised and centralised environmental management in Kenya, as in most African countries, was established under the colonial governments in the early 20th century. Most of these governments organised natural resource management in four branches, dividing wildlife, forestry, fisheries, and agriculture in pursuit of ‘governmen-tality’ (Foucault, 1991), which demanded rational forms of management of resources and populations through administrative apparatuses of the state, based on expert knowledge (Escobar, 1999).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".