The New Frontiers of Sovereign Investment
Bibliographic record
Abstract
Sovereign wealth funds (SWFs) can be effective tools for national resources revenue management. These state-owned investments, funded by commodity exports, foreign exchange reserves, or other national assets, are adaptable to the challenges posed by financial shocks and have been successfully employed in an increasing number of countries. The number of SWFs continues to grow, with the largest funds managing trillions of dollars in assets among them. However, given the significant variations among SWFs, it can be difficult to compare funds that differ in size, scope, and mandate. This book provides a sorely needed practical look at how these funds work – and how they should work.\nThe New Frontiers of Sovereign Investment combines the insights and experience of academic economists and practitioners from several funds to survey a diverse financial landscape and establish the challenging topical questions facing a broad range of SWFs today: Should they serve both economic development and financial returns, and how? Will responsible investment enhance long-term returns? How can fiscal rules for SWFs be improved to meet emerging economic challenges? The book considers these questions as they apply to both long-established and newer SWFs. Featuring contributions from sovereign wealth practitioners from Alberta's AIMCo, the Nigerian Sovereign Investment Authority, and the New Zealand Superannuation Fund, as well as analysis by scholars at the forefront of sovereign investment, this volume provides timely and much-needed information on these rapidly evolving institutions.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".