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Record W2789663336 · doi:10.1177/1070496518761994

Land-Use Planning, Digital Technologies, and Environmental Conservation in Tanzania

2018· article· en· W2789663336 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Environment & Development · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTanzaniaCitizen journalismEnvironmental planningEnvironmental resource managementGeospatial analysisGeographyLand useLand-use planningBusinessPolitical scienceEconomicsEngineeringCivil engineeringRemote sensing

Abstract

fetched live from OpenAlex

Participatory land-use planning (LUP) is often promoted as a solution to various environment-related challenges. In Tanzania, planning processes often represent a stage in the conversion of village lands to different uses, such as wildlife conservation or large-scale farming. LUP in Tanzania is frequently dominated by powerful local, national, or international elites, resulting in loss of rights over village land despite the opposition of many villagers. Contemporary planning involves digital technologies such as global positioning system units, which enable easier storage and sharing of geospatial data. Using assemblage theory, and based on key informant interviews conducted in Arusha and Kilimanjaro Regions of Tanzania in 2015, this article shows that LUP, particularly when it involves digital technologies, is used to not only to change land uses but also to strengthen linkages between different organizations, reinforce certain narratives of environmental change, and legitimize particular forms of external intervention.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.184
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