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
This paper compares and contrasts the hedge strategies through derivative instruments by Indian and USA corporate houses. The derivative instruments have little predictive power in explaining corporate hedging strategies both in the USA and Indian firms. The purpose of the study is to provide a setting where reconciling conflicting results from the literature may be appropriate and to compare different hedge strategies in a specific period in two different countries (USA and India). The evidence based on multivariate empirical relations between hedging in American firms and firm’s characteristics fails to provide any support for any of the tested hypotheses except for profitability represented by dividend yield. We conclude that the relationship between hedging and dividend yield in the proposed model is negative. The same analysis conducted for Indian companies has shown that there is no statistically significant explanatory variable for hedging; therefore, it is not dependent on any of the predicted theories of hedging. On the other hand, we find some significant relationships between firms’ characteristics. Large Indian firms use internal hedge strategies rather than market strategies, such as derivatives. The derivative market development then could play a major role in terms of risk management of firms across countries.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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