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Record W2156663788 · doi:10.1017/s0001972012000034

AFTER THE RUSH: LIVING WITH UNCERTAINTY IN A MALAGASY MINING TOWN

2012· article· en· W2156663788 on OpenAlexaff
Andrew Walsh

Bibliographic record

VenueAfrica · 2012
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsWestern University
Fundersnot available
KeywordsGold rushBoomConsumption (sociology)HistoryScale (ratio)GeographySociologyArchaeologySocial scienceEngineering

Abstract

fetched live from OpenAlex

ABSTRACT This article addresses the uncertainties of life in the once booming, but now declining, centre of northern Madagascar's sapphire trade. Although the characteristic features of small-scale mining boomtowns have become well known to many through research on gold, diamond and other rushes throughout Africa and elsewhere in the world, relatively little is known of what happens to such distinctive communities after they boom. What becomes of the unique social networks, consumption patterns, and world-views so often associated with these places when the supply of or demand for the particular commodities around which they have developed declines? Who leaves and who stays behind? How do those remaining in such places continue to earn livings and make meaningful lives despite the decline that surrounds them, and how do they make sense of their circumstances in light of memories of better times? This article addresses these and other questions as they relate to life after the rush in the northern Malagasy sapphire-mining and trading town of Ambondromifehy, arguing that the uncertainties faced by those who remain indicate new possibilities as much as continuing decline.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0280.010
Scholarly communication0.0060.003
Open science0.0020.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0060.000

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 head0.009
GPT teacher head0.179
Teacher spread0.170 · 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

Citations48
Published2012
Admission routes1
Has abstractyes

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