<i>RIPE</i> 30th anniversary special feature: looking back and looking forward in IPE
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.024 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.037 | 0.024 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".