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Displacement and Resistance Strategies of the Pastoralists Afar in Ethiopia

2025· article· en· W4416939346 on OpenAlexaffvenue
Gemechu Adimassu Abeshu

Bibliographic record

VenueAnthropologica · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsYork University
Fundersnot available
KeywordsPastoralismClanHerdingLivelihoodResistance (ecology)CommonsPoliticsIndigenousCONTEST

Abstract

fetched live from OpenAlex

This paper examines the resistance strategies of the displaced pastoralist Afar people in Northeast Ethiopia, focusing on the Lubakubo clan of the Dobi area. The Afar have historically practiced transhumant pastoralism, sustaining their livelihoods through the herding of livestock across arid and semi-arid landscapes. In 2004, a local “big man,” backed by the Ethiopian government and private investors, forcibly displaced members of the Lubakubo clan to facilitate commercial salt mining in Dobi, disrupting their socio-economic systems, cultural ties to the land, and traditional pastoral routes. Drawing on multi-sited ethnographic fieldwork conducted between 2015 and 2017, including in-depth interviews, participant observation, and archival research, this paper documents how the Afar mobilized lineage members, customary institutions, and strategic alliances to counter and contest the dispossession. These resistance strategies ranged from legal petitions and engagement with Afar traditional governance and conflict resolution structures to confrontation and symbolic acts reinforcing territorial claims. By situating these strategies within the broader political economy of resource extraction and displacement in the Horn of Africa, the paper contributes to scholarly debates on Indigenous resistance, land rights, and state–local power dynamics. The findings highlight the agency of displaced pastoralists in asserting their rights against state-backed commercial interests, while underscoring the resilience and adaptability of pastoralist systems under conditions of protracted displacement.

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 score0.209
Threshold uncertainty score0.636

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.260
Teacher spread0.254 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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