Extent of Commitment of Maritime Companies in Lebanon to Implementing the IAS 16
Bibliographic record
Abstract
The study examined the extent of commitment of maritime companies in Lebanon to implementing the International Accounting Standard (IAS) 16. It aimed at recognizing the extent to which maritime firms in Lebanon apply the International Accounting Standard (IAS) 16 by explaining the financial statements and their features and constituents. A five-point Likert style questionnaire was constructed as a study tool to collect information from the sample that consisted of 70 people who were accountants at maritime companies in Lebanon in addition to auditors of these companies. From the 70 questionnaires distributed, 63 were retrieved. The research concluded that maritime companies in Lebanon apply the IAS 16 in the income statement and the statement of financial position. The researchers recommended that the International Accounting Standards Board (IASB) should set up a clear and coordinated approach to deal with the issue of the periodic maintenance for ships, especially that the IAS 16 did not specify a preferred approach to settle this issue; rather, the IASB left it for the companies to choose the most convenient approach. They also recommended increasing disclosure of Lebanese maritime companies using the procedures followed in determining, depreciating and itemizing fixed assets in the financial statements.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".