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Record W4229441791 · doi:10.1080/14747731.2022.2058687

Insurgent infrastructures: bottom-up infrastructure-building in gold-mining regions in Colombia and Suriname

2022· article· en· W4229441791 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobalizations · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversity of New Brunswick
FundersVrije Universiteit Amsterdam
KeywordsAbandonment (legal)PoliticsHarmState (computer science)LegitimacyResource (disambiguation)Top-down and bottom-up designGold miningPolitical scienceEconomic geographyGeographyEngineeringLaw

Abstract

fetched live from OpenAlex

Critical analyses of infrastructural violence have mostly approached infrastructure as a top-down imposition that allows markets and state governments to expand and inflicts suffering on local populations. Here we take as our analytical starting point a different kind of infrastructural harm, namely the one that comes not from building the local environment, but from leaving it unbuilt. From this vantage point, we are foremost interested in local forms of socio-spatial organization that emerge in regions suffering from political abandonment. Drawing on fieldwork in gold-mining regions in Colombia and Suriname, we show that in resource frontiers where people criticize the state for being absent, informal mining stakeholders create their own infrastructures that provide them with a means to gain legitimacy and protest their social exclusion. While these ‘insurgent infrastructures’ take place outside the legal framework, they create the symbolic and material conditions for the state to appear.

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.

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 score0.188
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.011
GPT teacher head0.273
Teacher spread0.262 · 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