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Record W2328490321 · doi:10.3167/fcl.2015.730109

Global privatized power

2015· article· en· W2328490321 on OpenAlexaff
Maria Theresia Starzmann

Bibliographic record

VenueFocaal · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeological Research and Protection
Canadian institutionsMcGill University
FundersBrown University
KeywordsContext (archaeology)State (computer science)Power (physics)Cultural heritageTyingPolitical economyCultural heritage managementSociologyIslamPolitical scienceMilitarizationEconomyLawArchaeologyHistoryPoliticsEconomics

Abstract

fetched live from OpenAlex

The practice of archaeologists and other heritage specialists to embed with the US military in Iraq has received critical attention from anthropologists. Scholars have highlighted the dire consequences of such a partnership for cultural heritage protection by invoking the imperialist dimension of archaeological knowledge production. While critical of state power and increasingly of militarized para-state actors like the self-proclaimed Islamic State, these accounts typically eclipse other forms of collaboration with non-state organizations, such as private military and security companies (PMSCs). Focusing on the central role of private contractors in the context of heritage missions in Iraq since 2003, I demonstrate that the war economy's exploitative regime in regions marked by violent conflict is intensified by the growth of the military-industrial complex on a global scale. Drawing on data from interviews conducted with archaeologists working in the Middle East, it becomes clear how archaeology and heritage work prop up the coloniality of power by tying cultural to economic forms of control.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.014
Scholarly communication0.0050.005
Open science0.0000.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.001

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 head0.051
GPT teacher head0.276
Teacher spread0.225 · 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 designNot applicable
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

Citations1
Published2015
Admission routes1
Has abstractyes

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