Análise e gestão do risco: uma aplicação empírica à auditoria
Bibliographic record
Abstract
A análise e gestão do risco têm merecido nos últimos anos especial atenção por parte das organizações, que estão expostas regularmente a vários riscos de natureza interna e externa. O objectivo desta dissertação é salientar a importância da análise e gestão do risco para as organizações e para o trabalho desenvolvido no âmbito da auditoria, especialmente no que se refere ao desempenho organizacional. O estudo empírico incidiu sobre uma amostra constituída pelas empresas cotadas em mercado contínuo na Bolsa de Valores de Lisboa e pelas de Sociedades de Revisores Oficiais de Contas. A metodologia utilizada consistiu no envio de questionários com o objectivo de obter as informações necessárias para testar as hipóteses formuladas. Os resultados obtidos do estudo demonstram que as opiniões das empresas cotadas e das Sociedades de Revisores Oficiais de Contas face à importância da análise e gestão do risco nas organizações são semelhantes. - ABSTRACT: ln last years the analysis and management of risk has received special attention from those organizations that are usually exposed to internal and external risks. The purpose of this study is to underline the importance of analysis and management of risk for organizations and for audit work, especially concerning the organization performance. The empirical study is focused on a sample of Companies quoted on the Continuous Market of the Lisbon Stock Exchange and SROC' s. The methodology consisted on sending questionnaires in order to obtain the necessary information to test the hypotheses. The results show that the opinions of Companies and SROC's are similar concerning the importance of analysis and management of risk in organizations.
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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 | 0.048 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".