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Record W4390733771 · doi:10.1017/9781800109209.003

The Politics of Conservation Aid: The Development State and ‘Saving the Mau’

2023· other· en· W4390733771 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsState (computer science)Political scienceGeographyComputer scienceLawAlgorithm

Abstract

fetched live from OpenAlex

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).

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.012
Scholarly communication0.0080.007
Open science0.0010.006
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.028
GPT teacher head0.297
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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