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Record W2026479206 · doi:10.1177/1474474010384928

Racial narratives: Miskito and colono land struggles in the Honduran Mosquitia

2011· article· en· W2026479206 on OpenAlexfundno aff
Sharlene Mollett

Bibliographic record

VenueCultural Geographies · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of TorontoDartmouth College
KeywordsCONTESTNarrativeState (computer science)Context (archaeology)NationalismColonialismGender studiesSociologyEthnographyCivilizationAnthropologyEthnologyHistoryPolitical sciencePoliticsArchaeologyLawArt

Abstract

fetched live from OpenAlex

This article examines the multiple ways race and racialized processes are embedded in Miskito Indian and ladino colono land struggles in Honduras. In the context of more than 30 years of state refusals to formalize the boundaries of Miskito ancestral territories, this article interrogates the ways in which the state accommodates ladino colono encroachments inside Miskito space. State and colono challenges to Miskito customary claims echo early post-colonial narratives of integration under the Civilization Program. Drawing from ethnographic accounts, this article illuminates how meanings and practices are intertwined in the way land use production and racial hierarchies are mutually constituted. Thus, I argue that Miskito, state and colono narratives of land struggle draw on, contest and reinvigorate a longstanding state nationalist project of ‘whitening’ where racial imaginaries are encoded in environmental arrangements and assessed through ascendant conceptions of suitable and unsuitable land use practices.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.013
Scholarly communication0.0040.004
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.211
Teacher spread0.181 · 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 designQualitative
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

Citations47
Published2011
Admission routes1
Has abstractyes

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