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Record W4322102708 · doi:10.1080/09692290.2023.2176081

<i>RIPE</i> 30th anniversary special feature: looking back and looking forward in IPE

2023· article· en· W4322102708 on OpenAlexaff
Jennifer Bair, Juanita Elias, Daniela Gabor, Randall Germain, Aida A. Hozić, Alison Johnston, Saori N. Katada, Lena Rethel, Kevin Young

Bibliographic record

VenueReview of International Political Economy · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsPoliticsPublicationPolitical scienceLatin AmericansSociologyLaw

Abstract

fetched live from OpenAlex

The field of International Political Economy (IPE) has changed considerably since the inception of the Review of International Political Economy (RIPE). This 30th anniversary editorial reflects on how these changes have impacted the journal’s publications over the past ten years. Some trends are promising. RIPE has become more gender inclusive, as the share of women authors in submissions and publications has risen. RIPE has also resisted the ‘quantitative’ focus inherent within other political science/international relations journals, continuing to publish articles that demonstrate diverse qualitative methodologies (particularly case studies). Some trends are less encouraging. RIPE has struggled to move beyond its Anglo-American base, resulting in a paucity of published authors from institutions within the Global South, and limited articles that exclusively focus on IPE phenomena in Africa, the Middle East and Latin America (although Global South countries feature more heavily within trans-regional studies published by the journal, and have similar levels of coverage as the Global North). We conclude by introducing the RIPE 30th Anniversary Special Feature, a series of contributions by early career and emerging researchers reflecting on the history and future directions of the field of IPE. In commemorating RIPE’s 30th anniversary by opening - and continuing to open – its pages to new authors we seek to continue a long tradition and reaffirm the journal’s commitment to heterodoxy.

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)0.011
metaresearch head score (Gemma)0.047
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.047
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0050.003
Science and technology studies0.0040.004
Scholarly communication0.0240.010
Open science0.0040.006
Research integrity0.0110.016
Insufficient payload (model declined to judge)0.0370.024

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 head0.020
GPT teacher head0.335
Teacher spread0.315 · 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
GenreCommentary

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

Citations5
Published2023
Admission routes1
Has abstractyes

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