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Record W2460369071 · doi:10.3197/ge.2009.020404

History and Governance in the Ngorongoro Conservation Area Tanzania: 1959-1966

2009· article· en· W2460369071 on OpenAlexaboutno aff
Peter Rogers

Bibliographic record

VenueGlobal Environment · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsTanzaniaCorporate governanceWildlifePeriod (music)MaasaiPolitical scienceGeographyNational parkEnvironmental planningEnvironmental resource managementManagementArchaeologyEcology

Abstract

fetched live from OpenAlex

Abstract This article investigates the early history of the Ngorongoro Conservation Area (NCA), Tanzania, during the late 1950s and early 1960s, a period which has been overlooked in almost all the literature on the NCA. The article develops a governance perspective to argue that the NCA was heavily influenced by international thinking about wildlife conservation and Maasai pastoralism, and thus what was intended to be a multiple land use area was instead managed primarily as a national park. Several key episodes in this early history are dealt with in detail – the creation of the NCA; its early management difficulties; the role of Henry Fosbrooke, the NCA’s first Conservator; the impact of the international 1961 Arusha Conference on wildlife conservation; and the Canadian-funded management planning process of the mid-1960s. This leads to an exploration of some of the links between this period and contemporary practices in the NCA, and how practices of governance established over forty years ago still play a significant role in the present day. The case of the NCA illustrates the importance of appreciating the complexity of protected area governance and histories.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.178
Teacher spread0.170 · 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 designObservational
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

Citations3
Published2009
Admission routes1
Has abstractyes

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