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AbstractResearch Question/IssueThe objective of this study is to examine whether the effectiveness of internal governance is associated with internal control material weaknesses. We employ the concept of internal governance as the checks‐and‐balances mechanism that subordinate executives apply to the chief executive officer (CEO). We predict that with long horizons and long‐term interests aligned with firms' long‐term growth, subordinate executives may have the incentive to support a high‐quality internal control system, which is an important factor contributing to firms' long‐term success.Research Findings/InsightsUsing data on CEOs' and other highest paid executives' age and compensation to measure the effectiveness of internal governance, we empirically find consistent evidence that internal governance effectiveness is associated with higher internal control quality. In particular, we find that effective internal governance is related to a lower likelihood of firms reporting internal control material weaknesses, fewer material weaknesses in internal control (ICMWs), a lower chance of firms disclosing internal control weaknesses for multiple years, and a lower probability of firms reporting entity‐level and/or account‐level material weaknesses in internal control. We also show that among the two factors forming the internal governance measure, only subordinate executives' horizon is associated with the probability of firms disclosing ICMWs. Our further analysis reveals that the probability of reporting ICMWs is lower for growth firms with effective internal governance.Theoretical/Academic ImplicationsOur findings contribute to the literature on internal governance and internal control quality. The impact of the checks‐and‐balances mechanism inherent in internal governance on firms' investment in the internal control system and thus the probability of disclosing ICMWs has not received sufficient attention from accounting researchers. While prior studies focus on individual members of the management team, our finding implies that the quality of the internal control system is a result of the joint effort of the whole management team. Unlike the extant literature that captures only certain aspects of reporting quality and information disclosures, our study emphasizes the role of the horizon dimension of internal governance in enhancing the reliability of financial reporting (measured as the quality of the internal control system).Practitioner/Policy ImplicationsOur results shed light on the important role of subordinate executives in monitoring CEOs' short‐term interests.

Domain-specific metadata standards, including ontologies, markup languages, and technical interface specifications, are a necessary component of solutions for FAIR research data management with industrial applications. The Workshop on Domain Ontologies for Research Data Management in Industry Commons of Materials and Manufacturing (DORIC-MM 2021) discusses the state of the art, challenges, and perspectives for continuing innovation in this field. The present work comments on the landscape of semantic assets in the field of materials modelling, covering electronic, atomistic, mesoscopic, and continuum methods. Summaries are given of particularly promising lines of work, including the CAPE-OPEN interface standard, the XML schemas EngMeta, CML, and ThermoML, and the ontologies OntoCAPE, Metadata4Ing/Metadata4HPC, OSMO (the ontology version of MODA) and the VIMMP system of ontologies, and the domain-level modules of the Elementary Multiperspective Material Ontology (EMMO). For future work, it is recommended to emphasize advancing in accordance with five principles: 1. Diversification of technologies; 2. Observation of practices; 3. Realistic objectives; 4. Incentives for providing citable data and software; 5. Co-design of simulation and data technology. ; pp. 12 - 27 in Proceedings of DORIC-MM 2021, UKRI STFC, Daresbury, 2021. This work was supported by activities of the Innovation Centre for Process Data Technology (Inprodat e.V.), Kaiserslautern. The co-authors M.T.H. and B.S. acknowledge funding from the German Research Foundation (DFG) through the National Research Data Infrastructure for Catalysis-Related Sciences (NFDI4Cat), DFG project no. 441926934, and the co-author D.I. through the National Research Data Infrastructure for Engineering Sciences (NFDI4Ing), DFG project no. 442146713, both within the National Research Data Infrastructure (NFDI) programme of the Joint Science Conference (GWK). The co-authors G.G. and N.A.K. acknowledge funding from the European Union's Horizon 2020 research innovation ...

Domain-specific metadata standards, including ontologies, markup languages, and technical interface specifications, are a necessary component of solutions for FAIR research data management with industrial applications. The Workshop on Domain Ontologies for Research Data Management in Industry Commons of Materials and Manufacturing (DORIC-MM 2021) discusses the state of the art, challenges, and perspectives for continuing innovation in this field. The present work comments on the landscape of semantic assets in the field of materials modelling, covering electronic, atomistic, mesoscopic, and continuum methods. Summaries are given of particularly promising lines of work, including the CAPE-OPEN interface standard, the XML schemas EngMeta, CML, and ThermoML, and the ontologies OntoCAPE, Metadata4Ing/Metadata4HPC, OSMO (the ontology version of MODA) and the VIMMP system of ontologies, and the domain-level modules of the European Materials and Modelling Ontology (EMMO). For future work, it is recommended to emphasize advancing in accordance with five principles: 1. Diversification of technologies; 2. Observation of practices; 3. Realistic objectives; 4. Incentives for providing citable data and software; 5. Co-design of simulation and data technology. ; Submitted to the DORIC-MM 2021 workshop. This work was supported by activities of the Innovation Centre for Process Data Technology (Inprodat e.V.), Kaiserslautern. The co-authors M.T.H. and B.S. acknowledge funding from the German Research Foundation (DFG) through the National Research Data Infrastructure for Catalysis-Related Sciences (NFDI4Cat), DFG project no. 441926934, and the co-author D.I. through the National Research Data Infrastructure for Engineering Sciences (NFDI4Ing), DFG project no. 442146713, both within the National Research Data Infrastructure (NFDI) programme of the Joint Science Conference (GWK). The co-authors G.G. and N.A.K. acknowledge funding from the European Union's Horizon 2020 research innovation programme (H2020) under grant agreements ...

Prompt engineering in large language models (LLMs) in combination with external context can be misused for jailbreaks in order to generate malicious outputs. In the process, jailbreak prompts are apparently amplified in such a way that LLMs can generate malicious outputs on a large scale despite their initial training. As social bots, these can contribute to the dissemination of misinformation, hate speech, and discriminatory content. Using GPT4-x-Vicuna-13b-4bit from NousResearch, we demonstrate in this article the effectiveness of jailbreak prompts and external contexts via Jupyter Notebook based on the Python programming language. In addition, we highlight the methodological foundations of prompt engineering and its potential to create malicious content in order to sensitize researchers, practitioners, and policymakers to the importance of responsible development and deployment of LLMs.