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Record W7134994132

(Re)making resource frontiers through everyday violence and social movements in the uplands of mainland Southeast Asia

2022· other· en· W7134994132 on OpenAlexafffund
Kimberly Beth Roberts

Bibliographic record

VenueYorkSpace (York University) · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsYork University
FundersBritish Society for Plant PathologyElectricity Generating Authority of ThailandSaskatchewan Health Research FoundationHellenic Ministry of Environment and Energy
KeywordsResource (disambiguation)FrontierSocial movementAssemblage (archaeology)Mainland ChinaMainlandPower (physics)Politics
DOInot available

Abstract

fetched live from OpenAlex

In 2010, Myanmar began to transition from 48 years of military rule to a quasi-demilitarized and democratized government. This process attracted international attention to the country and increased investments in resource extraction. In this dissertation, I ask how historical relationships and present tensions between an assemblage of actors, resources, and spaces produce resource frontiers. To examine how state and non-state military-private partnerships and social movements co-constitute one another and resource frontiers, I developed a research approach using feminist collaborative methods. For three years (2015-2018) I worked with a team to conduct research with civil society organizations (CSOs) and in communities impacted by two sites of resource extraction: Tigyit Coal Mine and Power Plant and Mong Ton Hydropower Project. I argue that as a historical formation, resource frontiers intertwine with violence and capitalist extraction; and, as an unfolding process, they deeply affect everyday lives. To make this argument, I historicize resource frontiers and demonstrate how specific frontier projects dismantle and recreate property systems and nature-society relationships, often while leaving ongoing violence and exclusion in their wake. I highlight the interconnections between frontier-making, fragmented sovereignties, and conflict, while also demonstrating the everyday lived realities of frontiers. In this research, I conceptualize the way that globally-connected and historically-situated resource frontiers shape sovereignty, war, and access for localized frontier actors; and, in turn, how frontier actors remake nature, nation, and global trade. Through my focus of the everyday violence communities experience from resource frontiers and how fragmented sovereignties from an under-explored region interact with an under-explored actor (CSOs), I contribute to an expanded understanding of sovereignties, resource frontiers, and violence.

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)0.002
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.012
Scholarly communication0.0040.004
Open science0.0010.006
Research integrity0.0010.001
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 head0.013
GPT teacher head0.211
Teacher spread0.198 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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