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Record W4283459460 · doi:10.1017/s0001972022000262

Migration, authority and the gendered organization of labour in artisanal gold mining in Sierra Leone (and Mozambique)

2022· article· en· W4283459460 on OpenAlexaff
Blair Rutherford

Bibliographic record

VenueAfrica · 2022
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsCarleton University
Fundersnot available
KeywordsSierra leoneLivelihoodGold miningAgency (philosophy)EthnographyPoliticsTraditional authorityPolitical scienceEconomic growthGender studiesSociologyGeographySocioeconomicsSocial scienceLawAnthropologyArchaeology

Abstract

fetched live from OpenAlex

Abstract Recent studies of migration into artisanal and small-scale mining (ASM) have explored the motivations, strategic agency and professional trajectories of women and men miners who move to mine. In this article, I seek to shift the focus from the ‘push/pull’ factors in migration to consider the varied entanglements of mobility with authority and power relations pervasive through rural institutions. The importance – or lack of importance – of being a ‘migrant’ in these mining sites rests largely on the particular gendered cultural politics shaping livelihoods in the area. Drawing principally on ethnographic research in Tonkolili District, Sierra Leone, this article examines how migrant status for men and women working in the gold sites who came from other parts of the country is more marked than for Zimbabwean migrants working in artisanal gold mines in Manica, Mozambique. I argue that migrant status is marked by various authority and power relations in artisanal gold-mining sites when those controlling access to mining livelihoods, including access to residency in the mining communities, are able to emphasize contingent forms of belonging by migrants compared with those defined as ‘locals’. Critical to these differential forms of valuation is the particular organization of labour.

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 score0.238
Threshold uncertainty score0.157

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 head0.010
GPT teacher head0.186
Teacher spread0.177 · 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
Published2022
Admission routes1
Has abstractyes

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