Bibliographic record
Abstract
The primary objective of this review is to introduce Young's last book.A book in which he made use of all his experience to discuss and present insights on the effectiveness of governance systems and the emerging challenges of the Anthropocene 3 .Although it is not my intent to describe Young's academic success, trajectory, and achievements-since this noble task resided with well-known researcher, Ronald Mitchell (2013)-I could not fail to mention that, for more than 40 years, Oran Young, currently Professor Emeritus at the University of California, has promoted and stimulated knowledge about international institutions in various approaches (YOUNG, 2016).During this time, Young set out to seek explanations for successes and failures in international cooperation initiatives (YOUNG, 1999) and to understand the role of institutions, and he has done so with an impressive ability to innovate his thinking, identify new research questions and perspectives, develop new tools and conceptual models, challenge scholars in various areas of thinking, and produce results that contribute to both decision makers and scholars of global environmental governance.In "Governing Complex Systems.The social capital for the Anthropocene", the author brings his knowledge accumulated over the years to discuss the issue of environmental governance for a planet under constant pressure.In this book, he seeks answers to the governance of complex socioecological systems for recent periods marked by sudden and extreme changes with surprises, crises and periods of instabilities, plunged into great scientific uncertainties.The current understanding of global environmental governance owes much to Oran Young's numerous theoretical, empirical, and methodological contributions.His great scientific production and participation in numerous forums in multidisciplinary
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".