MétaCan
Menu
Back to cohort
Record W4382068218 · doi:10.1177/0958305x231159437

The dynamic relationship between military expenditure, environmental pollution, and economic growth in G7 countries: A wavelet analysis approach

2023· article· en· W4382068218 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnergy & Environment · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsKuznets curveCausality (physics)Granger causalityEconomicsEnvironmental pollutionPollutionDevelopment economicsMacroeconomicsEconometricsGeographyEnvironmental protection

Abstract

fetched live from OpenAlex

This study applies wavelet analysis to examine the interplay between treadmill of destruction theory and environmental Kuznets curve theory in G7 countries over the period 1970–2018. The results indicate that, in the short term, co-movement and causality exist between military expenditure and environmental pollution at different frequencies and times. Positive causality runs from military expenditure to environmental pollution in Italy, the United Kingdom, the United States, Canada, and Germany in different sub-periods. After adding economic growth as a control variable, positive causality runs from military expenditure to environmental pollution in Italy, the United Kingdom, Japan, the United States, Canada, and France in different sub-periods. Decision-makers can refer to the empirical results to consider the direction of policy and managerial implications at macroeconomic and microeconomic levels.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.014
GPT teacher head0.188
Teacher spread0.174 · 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