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
ABSTRACT This paper identifies jointly the optimal investment trigger and the optimal financing package for a corporate expansion project, using a real‐option ‘trade‐off’ model with agency problems. It also identifies the optimal initial capital structure of the firm (before the expansion). We show that it is generally optimal to use more debt than equity to finance the expansion. The other results are as follows: (i) existing debt has a negative effect, while the debt component of expansion financing has a positive effect, on investment; (ii) the debt component of the optimal expansion financing package is a decreasing function of the pre‐expansion leverage ratio (consistent with mean reverting leverage ratios), and is also decreasing in the magnitude of the expansion opportunity; and (iii) the optimal pre‐expansion leverage ratio is a decreasing function of both the firm's profitability and the magnitude of the growth opportunity. These relationships are generally consistent with empirical evidence, and help reconcile the trade‐off theory of capital structure with apparently contradictory empirical evidence.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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