Bibliographic record
Abstract
Abstract Significant academic and policy attention has focused on identifying the causes and consequences of the growth of traditional sovereign wealth funds ( SWF s). This paper, in contrast, highlights a new type of sovereign investment vehicle – sovereign patents funds ( SPF s) – that have emerged primarily in advanced industrialized economies, including France, South Korea and Japan. As defined here, SPF s are investment funds that seek to acquire intellectual property resources deemed strategically valuable in the pursuit of national economic objectives. This brief survey article considers the implications of SPF s in comparison to more traditional sovereign wealth funds. In doing so, it asks what emerging discourses on sovereign patent funds can learn from the sovereign wealth fund debate, and points to both similarities and important differences between these entities. While highlighting similarities between SPF s and traditional SWF s, the paper notes that patent funds are more explicitly political than their SWF counterparts. Following an overview of both SPF s and SWF s, the paper compares elements of the structure, behaviour, and response to these funds. Finally, while a full assessment of the activities and merits of SPF s lies beyond the scope of this article, the conclusion offers key lessons drawn from the SWF comparison.
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 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".