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Record W1543318790 · doi:10.7591/9780801458248

Constructing the International Economy

2015· book· en· W1543318790 on OpenAlexaboutno aff
Rawi Abdelal, Mark Blyth, Craig Alexander Parsons

Bibliographic record

VenueCornell University Press eBooks · 2015
Typebook
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsPolitical science

Abstract

fetched live from OpenAlex

Focusing empirically on how political and economic forces are always mediated and interpreted by agents, both in individual countries and in the international sphere, Constructing the International Economy sets out what such constructions and what various forms of constructivism mean, both as ways of understanding the world and as sets of varying methods for achieving that understanding. It rejects the assumption that material interests either linearly or simply determine economic outcomes and demands that analysts consider, as a plausible hypothesis, that economies might vary substantially for nonmaterial reasons that affect both institutions and agents' interests. Constructing the International Economy portrays the diversity of models and approaches that exist among constructivists writing on the international political economy. The authors outline and relate several different arguments for why scholars might attend to social construction, inviting the widest possible array of scholars to engage with such approaches. They examine points of terminological or theoretical confusion that create unnecessary barriers to engagement between constructivists and nonconstructivist work and among different types of constructivism. This book provides a tool kit that both constructivists and their critics can use to debate how much and when social construction matters in this deeply important realm. Contributors: Rawi Abdelal, Harvard Business School; Jacqueline Best, University of Ottawa; Mark Blyth, Brown University; Mlada Bukovansky, Smith College; Jeffrey M. Chwieroth, London School of Economics; Francesco Duina, Bates College; Charlotte Epstein, University of Sydney; Yoshiko M. Herrera, University of Wisconsin–Madison; Paul Langley, Northumbria University; Craig Parsons, University of Oregon; Catherine Weaver, University of Texas at Austin; Wesley W. Widmaier, Saint Joseph's University; Cornelia Woll, CERI-Sciences Po Paris

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0040.012
Scholarly communication0.0130.014
Open science0.0010.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0130.002

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 head0.061
GPT teacher head0.230
Teacher spread0.169 · 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 designTheoretical or conceptual
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

Citations98
Published2015
Admission routes1
Has abstractyes

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