Bibliographic record
Abstract
Third-party funding is a global phenomenon, although regulatory enforcement is local. Regulatory approaches vary widely from country to country and within countries, especially in federal legal systems, such as Canada, Australia, and the United States. The United States federal government is learning about third-party funding with an eye toward potential future regulation. Congress has been investigating funding, as evidenced by testimony in congressional hearings, proposed federal legislation, and a nonpartisan study on third-party funding by the Government Accountability Office. In addition, after more than a decade of observation, the United States Federal Civil Rules Advisory Committee recently formed a committee to explore whether to change the Federal Rules to address third-party funding. The United States federal government takes these steps against the patchwork quilt of conflicting and contrasting state regulations regarding third-party funding. This Article explores how federalism affects third-party funding in the United States. Specifically, it explores the likely effects of future third-party funding regulation at the federal level in conjunction with existing state regulations. Moreover, this Article presents various benefits and drawbacks that the United States federal government should consider when deciding whether to regulate TPF directly. It predicts whether the United States federal government will regulate third-party funding and, if so, how. Finally, this Article concludes by suggesting avenues for future inquiry.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".