Confronting Chaos: The Fiscal Constitution Faces Federal Shutdowns and (Almost) Debt Defaults
Bibliographic record
Abstract
Recent events raise the question of whether two near-failures in what scholars call the "Fiscal Constitution" may plunge the government into paralysis or chaos. In October 2013, a lapse in congressional appropriations shut the government down for two weeks. The shutdown furloughed hundreds of thousands of federal employees. It caused some agencies, such as the Internal Revenue Service ("IRS"), virtually to close their doors and to curtail their services. Simultaneously, and potentially even more devastating, the House of Representatives (alternatively "House") firmly refused during an extremely tense countdown to raise the statutory debt ceiling of the government. When the government hits that ceiling, it cannot borrow any more money. The government cannot meet all of its program obligations-like Social Security-without borrowing. As a result, once the debt ceiling is reached, the government may be unable to meet its debt interest obligations, causing it to default on the national debt. The nation would face calamity. Nonetheless, tremendous national pressure failed to budge recalcitrants in the House until, with the utmost reluctance, the House finally held a vote on October 17 to allow borrowing, just one day before the government would have hit the debt ceiling.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".