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Record W4402553841 · doi:10.1093/isq/sqae111

Anarchy as Architect: Competitive Pressure, Technology, and the Internal Structure of States

2024· article· en· W4402553841 on OpenAlexaff
Morgan MacInnes, Ben Garfinkel, Allan Dafoe

Bibliographic record

VenueInternational Studies Quarterly · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsArgument (complex analysis)Agency (philosophy)DisadvantageWelfareState (computer science)Welfare stateEconomicsEconomic systemLaw and economicsMarket economySociologyLawPolitical scienceComputer scienceSocial science

Abstract

fetched live from OpenAlex

Abstract The internal institutional structures of states greatly impact their citizens’ welfare. However, states are not at complete liberty to adopt any internal form. Competitive pressure arising from anarchy limits the range of viable domestic institutions to those that do not impose a significant disadvantage. We argue that technological change can alter the relative competitiveness of different state forms and, by extension, improve or degrade human welfare. We empirically support this argument through a macrohistorical survey of competitively significant technologies. We conclude that the true costs of international anarchy are greater than commonly appreciated, as competitive pressure may force states to evolve into forms detrimental to the welfare of their inhabitants. Moreover, the adoption of state forms that improve human well-being is often driven by technological change as much as human agency. Finally, the invention of seemingly beneficial technologies may decrease human well-being by improving the competitiveness of inegalitarian state forms.

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 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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.010
GPT teacher head0.315
Teacher spread0.305 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations3
Published2024
Admission routes1
Has abstractyes

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