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Record W4395047707 · doi:10.1111/1467-9655.14150

Making home alive again after war: Acoli <i>Kaka</i> ’s Indigenous land sovereignties in Northern Uganda

2024· article· en· W4395047707 on OpenAlexafffund
Lara Rosenoff Gauvin

Bibliographic record

VenueJournal of the Royal Anthropological Institute · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousEthnologyHistoryGeographyGenealogyLand rightsArchaeologyEcologyBiology

Abstract

fetched live from OpenAlex

Abstract After the war between the Ugandan government and the Lord's Resistance Army (1986‐2006), 90 per cent of the displaced rural population in Northern Uganda returned to small‐scale farming on their ancestral lands and their systems of communal land stewardship. At the time, there was much debate about transitional justice interventions to address war's violence, but in that same period over 85 per cent of Acoli chiefdoms saw affiliated clans, or kin‐based political communities ( kaka ), negotiate to write down their Indigenous governance constitutions for the first time. Acoli Kaka ’s return to their ancestral lands and small‐scale farming, and subsequent engagements with tekwaro – Indigenous knowledge – through constitution writing, served to strengthen Indigenous governance and law after their weakening in contexts of war and displacement. It is argued here that these engagements and negotiations rooted in the land, regardless of their outcomes, served to orient relationships away from the fragmenting, unprecedented, forced Acoli‐on‐Acoli violence experienced during the war. A resurgence of Acoli Kaka ’s Indigenous law and governance rooted in communal land stewardship is linked to relational repair and supports calls for transitional justice processes to nurture and respect Indigenous land rights. These ethnographic arguments also lend support to kaka ’s ongoing efforts towards clan unity ( ribbe kaka ) and to secure communal land sovereignties.

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 score0.296
Threshold uncertainty score0.499

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 head0.021
GPT teacher head0.252
Teacher spread0.231 · 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
Published2024
Admission routes2
Has abstractyes

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