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Record W3010376317 · doi:10.1080/00083968.2019.1674170

Necessity or choice: women’s migration to artisanal mining regions in eastern DRC

2020· article· en· W3010376317 on OpenAlexvenueno aff
Marie-Rose Bashwira, G. van der Haar

Bibliographic record

VenueCanadian Journal of African Studies / Revue canadienne des études africaines · 2020
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekEuropean CommissionGovernment of the United KingdomIrish Aid
KeywordsMerge (version control)CurseDemocracyVulnerability (computing)Resource cursePolitical scienceDevelopment economicsPolitical economyGeographySociologyNatural resourceComputer securityEconomicsLawPolitics

Abstract

fetched live from OpenAlex

Women have long remained invisible in representations of artisanal mining in eastern Democratic Republic of Congo. Based on original field data, this paper seeks to fill that gap. It shows how women come to mining sites with the hope of finding a degree of security, economic possibilities and the start of a new life. Contrary to what dominant discourses on the “resource curse” and sexual violence towards women have suggested, women may find a degree of protection in mining areas. We take the analysis beyond the “push” and “pull” factors with which migration is usually explained, to understand women’s motivation to move into mining areas as complex and changing. The study situates women’s movement to the mines within their life trajectories which are shaped by violence and various forms of insecurity. The notion of social navigation is brought in to understand how they cope with gender discrimination, challenges and risks in the mining economy. The paper shows how push and pull factors merge over time and how some women succeed in creating new sources of revenue and manage to mitigate the situation of vulnerability in which they find themselves.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.044
GPT teacher head0.234
Teacher spread0.190 · 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.

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

Citations22
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of African Studies / Revue canadienne des études africainesSame topicMining and Resource ManagementFrench-language works237,207