Metaphor Abuse in the Time of Coronavirus

In the time of Coronavirus, it is perhaps as good a time as any to comment on the use and abuse of metaphors. One of the worst instances of metaphor abuse—especially given the recent epidemiological crisis—is Lynne Tirrell’s notion of toxic speech. In the foregoing reply piece, I analyse Tirrell’s metaphor and reveal how it blinds us to the liberating power of public speech. Lynne Tirrell argues that some speech is, borrowing from field of Epidemiology, toxic in the sense that it harms vulnerable listeners. In this response piece, I summarize the main points of Tirrell’s toxic speech argument, map the underlying conceptual metaphor and pose three objections.

Which is a Better Method for Reporting Cash Flows from Operating Activities-Direct or Indirect Method?

This paper is a response to the ongoing debate in the accounting profession on whether the direct method is better than the indirect method when reporting cash flows from operating activities. The debate has its roots from the standard setters who prefer the direct method and are even debating on whether to make the direct method mandatory. The contention being that the direct method is a better method than the indirect method when reporting cash flows from operating activities since the disaggregation of its components suggests more disclosure. More disclosure in financial statements has been a cry from the financial statement users such as the creditors and investors. This qualitative argument will therefore show the merits of both the direct and the indirect method before getting to a conclusion on which method is better than the other. Further, it is a contribution to the ongoing debate in the accounting profession that can guide the standard setters as they deliberate on the possibility of making the direct method mandatory. In addition, a contention map and an argument map are used as roadmaps of the ideas being discussed in this study.

Effectiveness of computer-assisted argument mapping for comprehension, recall, and retention

Compared to traditional pen-and-paper presentation of information, computer-assisted argument mapping seems to be more efficient in developing lower order thinking skills such as memory and comprehension. The present study investigated the impact of argument map construction and reading via computer versus pen and paper on English as a foreign language (EFL) majors’ comprehension, recall, and retention of argumentative texts. To this end, 120 Iranian EFL undergraduates were divided into low and high proficiency levels after taking a language proficiency test. Next, they were randomly assigned to two experimental groups; each group received 12 sessions of argument mapping instruction, one via computer and the other via pen and paper. At the end of the term, participants randomly received two argument map sizes (small vs. large) and were given 15 minutes to read the maps. Then tests of recall and comprehension relevant to the maps were administered, followed by a test of retention within a two-week interval. The results revealed that after controlling for spatial and verbal covariates, the type of treatment had a significant effect on recall, retention, and comprehension, with the software group outperforming the pen-and-paper group; however, proficiency level and argument size did not show any significant effect.

CDMBE: A Case Description Model Based on Evidence

By combining the advantages of argument map and Bayesian network, a case description model based on evidence (CDMBE), which is suitable to continental law system, is proposed to describe the criminal cases. The logic of the model adopts the credibility logical reason and gets evidence-based reasoning quantitatively based on evidences. In order to consist with practical inference rules, five types of relationship and a set of rules are defined to calculate the credibility of assumptions based on the credibility and supportability of the related evidences. Experiments show that the model can get users’ ideas into a figure and the results calculated from CDMBE are in line with those from Bayesian model.

Argument Mapping and Content Fusion to Support the Analysis and Synthesis of Information in Online Discussions

While online conversations are very popular, the content generated by participants is very often overwhelming, poorly organized and often of questionable quality. In this article we use two methods, a text analysis technique, Vector Space Modeling (VSM) and clustering to create a methodology to organize and aggregate information generated by users using Online collaborative Argumentation (OA) in their online debate. An alternative to other widely used conversational tools such as online forums, OA is supposed to help users to join their efforts to construct a shared knowledge representation in the form of an argument map in which multiple points of view can coexist and be presented in the form of a well-organized knowledge object. To see whether this supposition comes into effect we first apply VSM to summarize argument map content as a document space and then use clustering to transform it to a limited number of higher order semantic categories. We apply the methodology to more than 3000 posts created in an online debate of about 160 participants using an online argumentation platform and we show how this methodology can be used to effectively organize and evaluate content generated by a large number of users in online discussions.