Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The extensive use of trade credit in all manufacturing sectors, despite its high cost, is an apparent puzzle that economists explain in terms of asymmetric information problems affecting financial markets. The financial constraints arising from credit rationing and limited access to stock markets suffice to induce firms to resort to trade credit as a supplemental source of funding. Nonetheless, empirical evidence shows that also large and liquid firms facing no binding financial constraints use substantial amounts of trade credit. We address this issue by modelling the financial policy of a firm that does not face a binding liquidity constraint but the risk of being constrained in the future. We characterise the optimal amount of trade credit held by such a firm, and we show that a positive probability of facing a liquidity constraint leads the firm to fund its inventories with trade credit, even if cheaper sources of funds are available. The rationale is that trade credit provides implicit coverage against liquidity risk. Therefore, the optimal amount of trade credit grows with the expected size of a possible liquidity shock and with the likelihood of its occurrence.
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.
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 | 0.001 |
| 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 it