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The purpose of this paper is to investigate the causal relationship between risk experienced within the real estate industry and that of the overall market in the UK context. The motivation behind this research is to investigate whether the real estate sector transmits risk to the wider marketplace and whether this phenomenon existed, or was exacerbated, during the most recent financial crisis.

The study was undertaken over a 20‐year timeframe, from 1990 to 2010, with special attention being awarded to the global financial crisis (GFC) period from 2008 to 2010. The paper first undertakes graphical modeling of market and industry volatilities in an attempt to identify which industry drives market uncertainty. This is followed by quantitative computation of industry‐specific volatility, which is employed in examining the relationship between these volatilities using block exogeneity/Granger causality tests. Rolling sample analysis and impulse response functions are employed as robustness tests to substantiate the main results.

First, the analysis confirms research that finance industry volatility is a leader in driving market volatility. Second, it expands on these findings to identify the real estate sector as being a key source of this causal relationship. It finds that real estate risk is the one that regularly drives finance industry volatility over the 20‐year sample period. Third, and most importantly, it emerges that the causal link between the real estate sector and market volatility is at its strongest leading up to the most recent financial crisis. More specifically, the real estate investment trusts sub‐sector of real estate industry volatility is the one that has the strongest unidirectional relationship with market‐wide volatility, both directly and indirectly, through driving the finance industry volatility during the GFC.

These findings are significant for market participants, such as pension funds, which need to protect their assets from a stock market crash. Furthermore, anticipating a downturn by observing the trends in real estate sector volatility is highly advantageous in informing their trading strategies now and into the future. Policy makers likewise need a signal of an impending credit crunch and can utilize real estate market statistics to pre‐empt a freezing up of the credit markets.

To describe how a digital storytelling project used in preservice elementary literacy methods courses expands the notion of video reflection and offers an intentional zone of contact in which preservice teachers create their own idealized vision of their future classroom.

Using the multimodal text as a point of departure, each researcher used a different analytical method to approach the data, allowing for examination of different aspects of the product and process of digital storytelling. These analysis methods include theoretically driven analysis based upon theories of Bakhtin (1981) and Vygotsky (1978), metaphor analysis, and performative analysis. This chapter describes the findings from each analytic lens, as well as the affordances of the multiple research lenses.

The results of the study shed light on how preservice teachers constructed a dialogue around their beliefs about themselves as teachers and visions of their future classrooms. The space between the real and the imagined provided a critical writing space where preservice teachers were able to vision their evolving identity and make visible their negotiation of intellectual, social, cultural, and institutional discourses they encountered. These artfully communicated stories engaged preservice teachers in creating new meanings, practices, and experiences as they explored possibilities and imagined themselves in their future classrooms. In these compositions, the preservice teachers maintained, disrupted, and/or reinvented classroom contexts to accommodate their own understandings of literacy teaching and learning.

The zones of contact that were consciously created in this digital storytelling assignment allowed teacher educators to provide the cognitive dissonance which research shows makes teacher beliefs more amenable. Additionally, asking preservice teachers to engage in the type of analysis described in this chapter may prove to be a useful avenue for helping to make the negotiation that took place during the composing of the digital stories more explicit for the preservice teachers.

This paper aims to draw from work in the field of English that questions the “limits of critique” (Felski, 2015) in order to consider the limits of critical literacy approaches to literature instruction. The study focuses on the relational and affective demands that resistant reading places on readers and texts.

Drawing from post-critical (Felski, 2015) and surface (Best and Marcus, 2009) reading practices in the field of English, the authors perform analyses of two recent articles that illustrate critical literacy approaches to literature instruction, drawing attention to the ways the resistant reading practices outlined in each article reflect Felski’s description of critique.

The authors’ readings of two frameworks of critical literacy approaches to literature instruction produce two key findings: first, in emphasizing resistant readings, critical literacy asks readers to take up a detective-like orientation to literature, treating texts as suspects; second, resistant reading practices promote a specific set of affective orientations toward a text, asking readers to cultivate skepticism and vigilance.

While the authors do not dismiss the importance of critical literacy approaches to literature instruction, the study makes room for other relational and affective orientations to literature, especially those that might encourage readers to listen to – and be surprised by – a text. By describing critical literacy through the lens of Felski’s work on critique, the authors aim to open up new possibilities for surprising encounters with literature.

Scant attention has been paid to the influence of professors on science, technology, engineering, and mathematics (STEM) students' learning and lives at the tertiary level. To fill this void, this chapter examines the influence of professors on students' entering and remaining in the STEM disciplines and pursuing STEM careers within the context of six funded STEM grants in the southern United States. We examine professor–student interactions using the students' storied experiences as the fodder for our narrative inquiry. We present narrative exemplars from which the following themes emerged: (1) agency as a student and agency as a human being, (2) development of students' multilayered identities, and (3) professors' engagement of themselves in their interactions with students. A discussion of learner-centeredness and professors' professional development in higher education concludes this study of professors' influence on students' learning and intended careers.

This study aims to share the development of new conceptual tools, which merge theories of critical whiteness studies (CWS), epistemic injustice and abolitionist teaching, applying them to the discourse of pre- and in-service teachers across the predominantly white institutions (PWIs) as they discuss antiracist teaching through the book Stamped and a series of online discussions.

This qualitative, collaborative practitioner inquiry derived data from video-recorded, online discussions, interviews and weekly research meetings. Critical discourse analysis revealed theoretical gaps and prompted the integration of additional theories, resulting in new conceptual tools, which are applied here to both “in the moment” exchanges between participants and individuals’ reflections in interviews.

Applying new conceptual tools to discussions of whiteness and race revealed how epistemic harm, microresistance and epistemic justice emerge in talk along with the importance of cultivating critical vigilance among antiracist educators.

This study elucidates how merging the conceptual frameworks of CWS, epistemic injustice and abolitionist teaching provides new tools for interrogating antiracism relative to whiteness in participants’ and researchers’ experiences. It challenges teacher educators, particularly at PWIs, to recognize how epistemic harm may be inflicted on students of color when centering whiteness in teacher education.

This study aims to examine whether the impact of international financial reporting standards (IFRS) on audit fees differs between early and late adopters.

The authors use robust econometric estimation on a sample of 314 firms from both early and late IFRS adopting countries.

The authors find that IFRS is positively and significantly associated with an increase in audit fees for early adopters, but the impact is very weak for late adopters and insignificant in some cases. The results on auditing time suggest that increase in audit fees around IFRS adoption is due to an increase in audit reporting lags. After accounting for pre- and post-years, the authors find that the relationship between IFRS and audit fees, as well as audit time for late adopters, is significant only in the adoption year. However, early adopters experience a significant increase in audit fees and audit time in the transition year to one-year post-adoption.

The findings imply that countries that are yet to adopt IFRS are less likely to experience a significant increase in audit fees audit time. Hence, is probable that the benefit of IFRS will outweigh the cost.

The results, therefore, suggest that early adopters paid a premium for been the first users of IFRS, which is consistent with any innovation. The study provides new insights by demonstrating that the consequences of IFRS differ between early and late adopters.

Near-fault pulse-like ground motions have distinct and very severe effects on reinforced concrete (RC) structures. However, there is a paucity of recorded data from Near-Fault Ground Motions (NFGMs), and thus forecasting the dynamic seismic response of structures, using conventional techniques, under such intense ground motions has remained a challenge.

The present study utilizes a 2D finite element model of an RC structure subjected to near-fault pulse-like ground motions with a focus on the storey drift ratio (SDR) as the key demand parameter. Five machine learning classifiers (MLCs), namely decision tree, k-nearest neighbor, random forest, support vector machine and Naïve Bayes classifier , were evaluated to classify the damage states of the RC structure.

The results such as confusion matrix, accuracy and mean square error indicate that the Naïve Bayes classifier model outperforms other MLCs with 80.0% accuracy. Furthermore, three MLC models with accuracy greater than 75% were trained using a voting classifier to enhance the performance score of the models. Finally, a sensitivity analysis was performed to evaluate the model's resilience and dependability.

The objective of the current study is to predict the nonlinear storey drift demand for low-rise RC structures using machine learning techniques, instead of labor-intensive nonlinear dynamic analysis.

To reflect what a teacher’s inner voice mediated by a video observation and discussion revealed about the process of change in literacy practices.

Nexus Analysis (NA) (Scollon & Scollon, 2004) was used in studying the teacher’s self-reflective dialogue for identifying the teacher’s (the first author) ways of being in the nexus of old and new literacy practices – in the process of change in the context of literacy practices. These ways of being were reflected on further in the study in the collaboration with the other authors.

The teacher’s ways of being balanced between “not knowing” and “knowing” connected both personal and professional aspects of learning.

Inner states of professional learning processes imply that both personal and professional support is needed in educational changes, such as the change in literacy practices. Video observations and discussion should thus not only concentrate on practical or theoretical issues of professional learning, but on promoting and offering safe spaces for reflection on subjective learning experiences.

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC in the early stage will save most of the women’s life. As there is an advancement in the technology research used Machine Learning (ML) algorithm Random Forest for ranking the feature, Support Vector Machine (SVM), and Naïve Bayes (NB) supervised classifiers for selection of best optimized features and prediction of BC accuracy. The estimation of prediction accuracy has been done by using the dataset Wisconsin Breast Cancer Data from University of California Irvine (UCI) ML repository. To perform all these operation, Anaconda one of the open source distribution of Python has been used. The proposed work resulted in extemporize improvement in the NB and SVM classifier accuracy. The performance evaluation of the proposed model is estimated by using classification accuracy, confusion matrix, mean, standard deviation, variance, and root mean-squared error.

The experimental results shows that 70-30 data split will result in best accuracy. SVM acts as a feature optimizer of 12 best features with the result of 97.66% accuracy and improvement of 1.17% after feature reduction. NB results with feature optimizer 17 of best features with the result of 96.49% accuracy and improvement of 1.17% after feature reduction.

The study shows that proposal model works very effectively as compare to the existing models with respect to accuracy measures.