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Record W3081883768 · doi:10.5539/ijef.v12n9p111

Extent of Commitment of Maritime Companies in Lebanon to Implementing the IAS 16

2020· article· en· W3081883768 on OpenAlexvenueno aff
Rani Shakaroun, Hasan El-Mousawi, Joumana Younis

Bibliographic record

VenueInternational Journal of Economics and Finance · 2020
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsAccountingAuditBusinessPosition (finance)Sample (material)Financial statementIncome statementPoint (geometry)Likert scaleFinancial statement analysisFinanceBalance sheetFinancial ratio

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.139

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.233
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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