Impact of COVID-19 Outbreak on Financial Reporting in the Light of the International Financial Reporting Standards (IFRS) (An Empirical Study)
Bibliographic record
Abstract
The outbreak of a novel type of Coronavirus (COVID-19) in the majority of countries around the world has had many negative implications on almost all aspects of life. Currently, about a quarter of the population of Earth is quarantined at their homes, social distancing is effective everywhere, almost all industries have ceased their activities, and various businesses are either closed down or working from home. Procedures taken by governments or local authorities to improve their ability to contain the outbreak have impacted the global economy, which in turn will have many consequences on financial reporting of organizations. This study examines the impact of the novel Coronavirus outbreak on financial reporting of organizations from the viewpoint of Certified Public Accountants in Lebanon. The researchers have used a descriptive-analytical approach and have constructed a well-structured five-point Likert style questionnaire as the study tool. The questionnaire was distributed to a sample chosen from the population of certified public accountants in Lebanon. The random sample consisted of 300 practitioners of the profession, and 221 of them responded; all of which were valid for testing and analysis. The study reached some important findings mainly that the COVID-19 outbreak has had a significant impact on the financial reporting of businesses according to the opinions of Certified Public Accountants (CPAs) in Lebanon, and the researchers had some recommendations as a result.
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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 | 0.005 | 0.014 |
| 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 | 0.000 | 0.001 |
| 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".