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Record W2101721719 · doi:10.1080/13648470.2014.918931

‘The land is now not fertile’: social landscapes of hunger in south-eastern coastal Tanzania

2014· article· en· W2101721719 on OpenAlexafffund
Mai‐Lei Woo Kinshella

Bibliographic record

VenueAnthropology and Medicine · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of British Columbia Hospital
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTanzaniaFood securityGeographyFocus groupEconomic growthSocioeconomicsEthnographySociologyPolitical scienceAgricultureEconomics

Abstract

fetched live from OpenAlex

Based on ethnographic fieldwork in a coastal village in south-eastern Tanzania, this paper examines how social inequalities and social suffering become embodied in the lived experiences of hunger. The paper explores local meanings of food, fertility and hunger and how these concepts interconnect and materialize on a landscape impacted by two large-scale conservation and development projects, the Mtwara Development Corridor and the Mnazi Bay Ruvuma Estuary Marine Park. Fourteen in-depth interviews Sinde villagers were conducted to elicit narratives about their food experiences in addition to 24 hour food recall and pile sort to explore local taxonomies of food. One focus group discussion with six women was also conducted. The study finds displacement from resources by the conservation and development projects has exacerbated existing food security issues of irregular rains, increasing food prices and malnourished bodies. The downward cycle of food insecurity has local villagers worried about the viability of their community's future, embodied in the health of local children and their performance in school. Increasing food insecurity is internalized within the community as infertility where the health of the landscape is connected to the health of society.

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 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 score0.058
Threshold uncertainty score0.420

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 science0.0000.000
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 head0.247
Teacher spread0.234 · 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 teacher head, 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
Published2014
Admission routes2
Has abstractyes

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