The Impact of Covid-19 on the Type of Auditor’s Opinion: Evidence from the Largest Non-Listed Portuguese Companies
Bibliographic record
Abstract
In 2020, the world was surprised by a pandemic caused by Covid-19, which emerged and devastated the entire world economy, forcing several companies to adapt to a new health reality that affected the lives of people and the companies themselves, forcing the population to readapt. In this follow-up, the present research arises with the aim of analysing the impact of Covid-19 on the Audit Report of the largest Portuguese unlisted companies, namely regarding the type of opinion. This research included the analysis of 114 audit reports, referring to the years 2018 (pre-pandemic), 2020 (during pandemic) and 2022 (post-pandemic) of the largest and best Portuguese non-listed companies, according to Exame Magazine for the year 2022. The data were collected through a content analysis of the Audit Reports of these companies, and then statistically treated through a bivariate analysis using association tests between the variables (dependent and independent). The results obtained proved that Covid-19 did not significantly influence the type of opinions issued. However, we were able to verify that there is a lower probability of the Big four firms issuing modified opinions. This study, in a way, contributed to the literature on this topic, clarifying some impacts of the pandemic on audit work, as well as the impacts of other variables on the Audit Reports.
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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 | 0.016 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".