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Treadmill of Destruction

2016· other· en· W4229946739 on OpenAlexaff
Gregory Hooks, Chad L. Smith

Bibliographic record

VenueThe Blackwell Encyclopedia of Sociology · 2016
Typeother
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHarmGeopoliticsCompetition (biology)ResizingWorld War IIPolitical scienceGeographyDevelopment economicsEconomyBusinessLawEconomicsInternational tradeEcologyPolitics

Abstract

fetched live from OpenAlex

Abstract The treadmill of destruction framework calls attention to the environmental harm resulting from war and war‐making. Geopolitical competition and arms races push military organizations to voraciously consume nonrenewable resources and dump highly toxic waste products. War devastates the lived environment for millions of people and poisons large tracts of land. In the twenty‐first century, the absolute size of the military in affluent nations of the global north are shrinking and this may result in a “greening” of the military in these affluent nations. But the environmental risks of war have not disappeared: they are being shifted to the vulnerable peoples and places in the global south.

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)0.004
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: Other · Consensus signal: Other
Teacher disagreement score0.047
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0040.026
Scholarly communication0.0100.014
Open science0.0020.006
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0470.004

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 head0.012
GPT teacher head0.281
Teacher spread0.270 · 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
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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