The Role of Operating Income on the Internal Long-Term Financing Decisions
Bibliographic record
Abstract
This paper aims to demonstrate the roles and importance of the operating income of the management of Jordanian industrial companies, and the extent to which they depend on it to make the necessary financial decisions in order to meet their long-term needs. The data was collected from the financial reports of companies representing the study community for the period 2012-2016, after being classified by different industrial sectors. The research employed the operating incomes as an independent variable and long-term internal financial decisions as a dependent variable. The results of the data analysis showed a disparity between the different industrial sectors. The Engineering and Construction industries achieved the highest average of the ratio of long-term internal financing by 0.986, due to the importance of this activity on the Jordanian economy and the size of the high investment in it from the researchers' point of view. The lowest average was shown in the Paper and Cardboard sector by 0.550, due to the lack of investments in it compared to other industrial sectors that are more important to the Jordanian economy. The overall annual average of long-term domestic financing for all sectors was 0.892. The results also showed a statistically significant role for operating income in long-term internal financial decisions by the management of Jordan's public industrial joint stock companies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".