Bibliographic record
Abstract
Abstract The United States' bankruptcy system faces a major problem: many consumers are too poor to file for bankruptcy, usually because they cannot afford the necessary attorney fees. Some consumers appear to spend months trying to save the funds to pay their attorneys, thus either delaying their bankruptcies or foregoing bankruptcy altogether when they fail to save enough money. Others file for repayment bankruptcy in order to pay attorney fees during the case, when liquidation bankruptcy is usually a better fit for consumers with low incomes and low asset levels. The most recent comprehensive bankruptcy reform, the Bankruptcy Abuse Prevention and Consumer Protection Act (BAPCPA), exacerbated these problems by implementing additional procedural requirements that resulted in attorneys raising their fees. These problems have led to calls for administrative bankruptcy, especially for low‐income, low‐asset (LILA)/no‐income, no‐asset (NINA) debtors. Administrative bankruptcy would make bankruptcy more accessible by lowering access costs, for example, by eliminating the need for consumers to hire attorneys. Administrative programs in the United States, however, have a history of long‐term decline, especially when these programs serve low‐income people. It has become a cliché that poor people's programs become poor programs. A better solution would be to eliminate the procedural requirements imposed by BAPCPA and simplify the decision consumers must make about which type of bankruptcy to use.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".