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
<p>The main purpose of this study is to understand the determinants of corporate hedging in emerging markets. The dependent variable, hedging, is estimated by a categorical variable. This process necessitates the usage of logistic regression. The analysis is conducted using data from non-financial companies listed in Borsa Istanbul (BIST) between 2010 and 2014. Evidence reveals that the cost of underinvestment has the highest impact on the likelihood of hedging. Firms with higher cost of underinvestment are more likely to use financial derivatives. The second most important determinant of hedging is growth opportunities. Interestingly, firms with greater growth opportunities are less likely to use derivatives in emerging markets. Results indicate that firm size, foreign sales, profitability, and dividend yield are the other predictors that increase the likelihood of hedging. On the other hand, growth opportunities, free-float rate, interest coverage ratio, and leverage have a negative relationship with the possibility of using financial derivatives.</p>
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 | 0.002 |
| 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