The impacts of third-party funding on cost decisions in investment arbitration
Bibliographic record
Abstract
The involvement of Third-party Funding (TPF) in investment arbitration disrupts the balance between the parties to an arbitration. Though a party’s reliance on external funding represents its impecuniousness to participate in an arbitration, many financially sound investors take TPF to reduce the risk associated with bringing a claim or are unwilling to stick their working capital in arbitration. The existence of TPF in arbitration is a material factor in deciding an order for security for arbitration costs. The third-party funder funds an investor to initiate arbitration and gets benefits from a cost award. However, the funder does not share an investor’s responsibility to pay an adverse cost. The funder’s immunity from adverse costs aggravates the demand for security for costs in a funded arbitration. While a claimant’s reliance on TPF is considered a material factor in issuing an order for security for its cost, this consideration, counter-wise, legitimizes the cost of funding as arbitration costs. Accordingly, the funding cost can be recoverable through an adverse cost award. The TPF consideration in an order for security for costs makes the funding arrangement a part of the arbitration proceedings. If the funding position of a party is considered in deciding an application for security for costs, it deserves equal consideration in awarding adverse arbitration costs. Establishing the funding cost as arbitration costs will increase the cost of the international arbitration and unjustly transfer public money to private entities.
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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 | 0.029 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 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".