The Application of International Accounting Standard’s Requirements No. (20) in Jordanian Chemical Detergents Industry Companies
Bibliographic record
Abstract
The study aimed at identifying the extent to which Jordanian chemical detergents industry companies applying the requirements of international accounting standard No. (20). A questionnaire has been designed for this purpose and distributed to the external auditors of these companies of (50) auditors / questionnaire, (30) questionnaires were recovered and were suitable for analysis, with recovery rate reached to (60%). Resolution data was analyzed using (SPSS), and a number of statistical techniques through descriptive statistics, arithmetic means, standard deviations and percentages. The results of the study showed that Jordanian chemical detergents industry companies do not apply the requirements of international accounting standard No. (20), and there are difficulties that limit the application of the mentioned standard in a high degree. The study recommended urging Jordanian chemical detergents industry companies to implement the requirements of international accounting standard No. (20), in addition to helping Jordanian chemical detergents industry companies to reduce the difficulties of application of international accounting standard No, (20) and treated them radically.
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.
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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".