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 study aims to identify the capital structure determinants of the listed Russian firms. The determinants are the factors that would affect firm financial leverage. The capital structures theories and their applications are considered in the article. The study is based on a sample of 48 publicly-traded non-financial firms over the period 2009-2015. The random-effects model is employed for estimations while the OLS approach is used to measure the industry impact on capital structure. It is found that the most significant capital structure determinants of Russian firms are industry mean leverage, firm size with positive effect and growth opportunities with negative one. Profitability, non-debt tax shields and the stock market conditions with negative impact are less important. Business risk, growth opportunity measured as capital expenditures to total assets, tangibility of assets, uniqueness of assets, average tax rate, industry group of Energy firms, lending and inflation rates are irrelevant determinants. Another finding is that the Oil & Gas and Metal firms tend to have lower debt level compared to the firms from other industries.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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