A critical evaluation of the new <scp>cram‐down</scp> tool in Singapore's restructuring regime
Bibliographic record
Abstract
Abstract Singapore has recently reformed its insolvency regime in its efforts to be an international restructuring hub. To that end, Singapore has attempted to take an autochthonous approach in adapting several of the legal tools from the US Chapter 11 for reforming its own restructuring regime. This article seeks to critically evaluate the cross‐class cram‐down mechanism in Singapore, which has been implemented with caution and novelty. While such an approach seeks to protect both the interest of its shareholders and creditors, it might lead to a regime that is undesirable for Singapore, considering its pursuit to be an international restructuring hub. In particular, it will be argued that Singapore's cram‐down mechanism is susceptible to risks of hold‐ups by both shareholders and creditors. Additionally, from the ex ante perspective, the cram‐down mechanism does not promote expediency in parties' negotiation. The implementation of a novel cram‐down arrangement would also inextricably bring about uncertainty to its application. A lacklustre engagement of the cram‐down tool in practice might ensue, which could hamper Singapore's plan to organically develop and fine‐tune the cram‐down regime down the road.
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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 | 0.050 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".