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In this article, I seek to show how invocations of political correctness still work in nuanced ways to close down debates and trivialise issues, and effectively slow down progress towards achieving an anti‐racist society. I also begin to identify the typologies of the myriad ways in which it presents.

It is an autobiographical account, with evidence gleaned from personal experiences as an equalities trainer and from discussions arising as part of supporting and mentoring other trainers.

Political correctness has not gone away: it is still invoked by politicians, the media and individuals within organisations to attack anti‐racism and anti‐racist learning.

The research is limited by its autobiographical and inevitably subjective nature. However, the standpoints of resistance that I have chosen to discuss are those that have arisen frequently and repeatedly and from across a range of public sector and quasi‐public sector organisations.

Despite its limitations, the article clearly demonstrates that political correctness remains an important topic for academic and practitioner debate, and it also identifies the need for further work in developing training to combat its destructiveness.

An original and firsthand account of anti‐racist training from the front line is provided. Its value lies in its experiential origins and its potential as a basis for further work.

Business process models, while primarily intended for process documentation, communication, and improvement, are often also used as input for developing process-oriented software systems (Ouyang et al., 2009). Ensuring correctness, handling complexity, and improving reusability and maintainability of business process models are important for all these goals. The purpose of this paper is to propose an aspect-oriented business process modeling and correctness controlling method based on Petri nets to satisfy these goals.

The aspect-oriented paradigm provides a proper mechanism to modularization, and thus reduces the complexity of models, and also improves reusability and maintainability. However, weaving aspects into base processes may bring in mistakes or errors. To ensure correctness of modeling, this paper presents a formal approach to modeling aspect-oriented business processes and a method to ensure modeling correctness. Petri net is used as the process modeling language and its analysis techniques are applied to analyze the correctness of modeling. Two types of correctness, specifically, aspect-aspect correctness and base-aspect correctness are analyzed. A real banking process model is studied systematically in the case study to evaluate the approach and the performance assessments are conducted to show the cost and effect of the approach.

Designing aspect-oriented business process models help organizations reusing the model elements to reduce redundancy of their model repository, improving their maintainability, and supporting them to adapt to the changes of business requirements with flexible modeling. It is important to stress that the correctness of business process modeling is important in ensuring the quality of the models, especially in the safety-critical business domains, such as financial business domain.

In this paper, separation of concerns is used to separate the cross-cutting activities and core activities in accordance with the different functions of these activities, and an approach to modeling aspect-oriented business processes is proposed. First, the cross-cutting activities are encapsulated as aspects, while core business activities are modeled as base processes. Then, according to the correctness requirements of business process models, based on the weaving mechanisms of aspect-oriented approach, weaving correctness is defined. Weaving correctness controlling methods between multi-aspects and between aspects and base processes are designed. Errors or mistakes of aspect-oriented business process modeling are prevented during the procedure of modeling to ensure error-free business process modeling.

The need for assuring correctness of business processes in enterprises is widely recognised in terms of business process re-engineering and improvement. Formal methods are a promising approach to this issue. The challenge in business process verification is to create a formal model that is well-aligned to the reality. Process mining is a well-known technique to discover a model of a process based on facts. However, no studies exist that apply it to formal verification. This study aims to propose a methodology for formal business process verification by means of process mining, and attempts to clarify the challenges and necessary technologies in this approach using a case study.

A trading company simulation model is used as a case study. A workflow model is discovered from an event log produced by a simulation tool and manually complemented to a formal model. Correctness requirements of both domain-dependent and domain-independent types of the model are checked by means of model-checking.

For business process verification with both domain-dependent and domain-independent correctness requirements, more advanced process mining techniques that discover data-related aspects of processes are desirable. The choice of a formal modelling language is also crucial. It depends on the correctness requirements and the characteristics of the business process.

Formal verification of business processes starting with creating its formal model is quite new. Furthermore, domain-dependent and domain-independent correctness properties are considered in the same framework, which is also new. This study revealed necessary technologies for this approach with process mining.

This study examines how the distribution of opinions and social status combine in a collectively oriented task group to affect perceptions about the correctness of a final decision.

We relied on data from a controlled laboratory experiment to test a series of theoretically derived hypotheses.

The study shows that both the distribution of opinions and status affect perceptions of correctness. It also establishes that the effects of status on uncertainty are strongest when the group is initially evenly split about the correctness of an opinion, and that like the distribution of opinions, the effects of status on uncertainty are curvilinear.

Previous research shows that by integrating research on faction sizes with status characteristics theory (SCT), more accurate predictions of social influence are possible. Assumed therein is that people use information about the distribution of opinions and status to reduce uncertainty about correctness of a choice. The current study establishes this point empirically by examining the effects of the distribution of opinions and status in a four-person, collectively oriented task group. Future research should consider groups of different sizes and other moderating factors.

This study advances and elaborates upon previous research on social influence that integrates research on faction sizes with SCT.

Political correctness, also known as “PC,” has generated much discussion on both the Right and the Left. The greatest debate comes from college campuses. Political correctness derives from the principle that ethnic diversity, i.e., multiculturalism, can and should be preserved and protected. Ironically, the term originated in the Marxist era, when it was used to enforce conformity in the advancement of a particular Marxist view. The term became obsolete until it was revived in the 1980s (D'Souza, 1991).

Describes the phenomenon of political correctness and its effect on the way brand marketers conduct their business. Discusses in detail the effect of this ideology on brand association, brand symbolism, target marketing, promotional messages and brand features; provides case studies. Presents first, a brief discussion of political correctness with a working definition; then the differences between politically correct and politically incorrect brands. Uses the examples of Dakota cigarettes and Stroh′s beer to show how brand symbolism is affected by political correctness. PowerMaster beer, Uptown cigarettes and Nike athletic shoes are examples of politically incorrect target market selection. Benetton is controversial, yet politically correct, as is Ben and Jerry′s ice‐cream, with respect to promotional messages. Fur coats and Barbie dolls have incorrect product features, while The Body Shop has correct ones. Recommendations for brand marketers include two possible strategies: prevention and proaction; or reaction (damage control). Observes that as marketing is a highly visible activity, marketers must act to protect brand equity regardless of their beliefs about the political correctness phenomenon.

This study aims to evaluate a method of building a biomedical knowledge graph (KG).

This research first constructs a COVID-19 KG on the COVID-19 Open Research Data Set, covering information over six categories (i.e. disease, drug, gene, species, therapy and symptom). The construction used open-source tools to extract entities, relations and triples. Then, the COVID-19 KG is evaluated on three data-quality dimensions: correctness, relatedness and comprehensiveness, using a semiautomatic approach. Finally, this study assesses the application of the KG by building a question answering (Q&A) system. Five queries regarding COVID-19 genomes, symptoms, transmissions and therapeutics were submitted to the system and the results were analyzed.

With current extraction tools, the quality of the KG is moderate and difficult to improve, unless more efforts are made to improve the tools for entity extraction, relation extraction and others. This study finds that comprehensiveness and relatedness positively correlate with the data size. Furthermore, the results indicate the performances of the Q&A systems built on the larger-scale KGs are better than the smaller ones for most queries, proving the importance of relatedness and comprehensiveness to ensure the usefulness of the KG.

The KG construction process, data-quality-based and application-based evaluations discussed in this paper provide valuable references for KG researchers and practitioners to build high-quality domain-specific knowledge discovery systems.

It is the contention of this paper that the state of siege on the part of students against academics and administrators of the 1960s has been replaced by one led by university administrators who are now waging a war of “political correctness” against students and faculty. These administrators were in large part the radicals of the 1960s. The present paper attempts to analyze the effects of government involvement in this industry on this phenomenon and the role of free speech rights in the presence and absence of marketplace considerations.

Discusses political correctness and the economics of higher education.

The contention is that competition brings about a better product at a lower price, and that the educational sector is no exception to this general rule. If free enterprise were allowed to operate in this context, much of these difficulties would disappear.

The paper offers insights into today's higher education industry and how economic analysis can explain the current “state of siege” on university campuses.

The primary objective of this research is to investigate the impact of task complexity on users’ performance in the context of using computer‐based technologies with auditing tasks. A laboratory experiment was performed with 121 undergraduate auditing students. The task complexity is defined in form of structured and unstructured tasks. The dependent variables were accuracy in solving problem and certainty of the correctness of solution. The results suggested that the effect of task complexity on accuracy in solving problems was significant for structured tasks. In addition, the significant effect was also found on certainty of the correctness of solution for structured tasks. The findings suggest the strategy for improving user performance in solving problem and certainty of the correctness of solutions toward a given task by giving users a structured task to perform. In addition, the findings also suggest the need for research on how to benefit from using computer‐based technologies in the context of solving structured task.

This paper aims to explore drivers of the effectiveness of risk assessments in risk workshops.

This study uses an agent-based model to simulate risk assessments in risk workshops. Combining the notions of transactive memory and the ideal speech situation, this study establishes a risk assessment benchmark and then investigates real-world deviations from this benchmark. Specifically, this study models limits to information transfer, incomplete discussions and potentially detrimental group characteristics, as well as interaction patterns.

First, limits to information transfer among workshop participants can prevent a correct consensus. Second, increasing the required number of stable discussion rounds before an assessment improves the correct assessment of high but not low likelihood risks. Third, while theoretically advantageous group characteristics are associated with the highest assessment correctness for all risks, theoretically detrimental group characteristics are associated with the highest assessment correctness for high likelihood risks. Fourth, prioritizing participants who are particularly concerned about the risk leads to the highest level of correctness.

This study shows that by increasing the duration of simulated risk workshops, the assessments change – as a rule – from underestimating to overestimating risks, unraveling a trade-off for risk workshop facilitators. Methodologically, this approach overcomes limitations of prior research, specifically the lack of an assessment and process benchmark, the inability to disentangle multiple effects and the difficulty of capturing individual cognitive processes.