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The purpose of this paper is to assess student perceptions of their learning during reference transactions and to evaluate a note-taking strategy developed to improve the quality of learning during reference encounters.

Students were surveyed following a reference interaction and were asked, “What did you learn today?”. Based on the authors results, librarians developed a Reference Notes form encouraging librarians and students to take notes during reference transactions, highlighting terms, concepts and strategies. The forms were assessed with a modified version of the initial student survey to determine their effectiveness. Student survey results were analyzed, and librarians also provided feedback via surveys and discussions.

Initial results indicated that students retained concrete concepts like the names of previously unknown databases. With the implementation of Reference Notes, students were more likely to report learning broad-based concepts like narrowing a search, brainstorming keywords and search mechanics. Librarians and students felt the form was an effective reference tool.

This is an indirect method of assessing student learning, relying on students’ self-reports. Without the opportunity to pre-define learning objectives for a reference transaction, the authors were unable to assess student learning directly.

Many librarians write down some information during reference transactions. A more systematic approach to taking notes may improve the learning potential of the reference encounter.

This project demonstrates that student learning assessment is an important tool for evaluating reference services. Through student learning assessment, librarians can develop strategies, such as the authors Reference Notes forms, to increase the quality of learning during reference transactions.

Investigates the differences in protocols between arbitral tribunals and courts, with particular emphasis on US, Greek and English law. Gives examples of each country and its way of using the law in specific circumstances, and shows the variations therein. Sums up that arbitration is much the better way to gok as it avoids delays and expenses, plus the vexation/frustration of normal litigation. Concludes that the US and Greek constitutions and common law tradition in England appear to allow involved parties to choose their own judge, who can thus be an arbitrator. Discusses e‐commerce and speculates on this for the future.

This issue is a selected bibliography covering the subject of leadership.

Intercultural competence has been found to be increasingly important. The purpose of this paper is to understand how intercultural competence impacts service providers’ ability to recognition faces of both black and white consumers.

Two experiments were administered to understand how intercultural competence impacts recognition of black and white consumer faces.

The authors find that the more intercultural competence that respondents report with blacks, the better they are at distinguishing between black regular customers and black new shoppers in an experiment. The authors find no impact of intercultural competence on the ability of respondents to differentiate between white consumers. These findings hold for respondents in the USA and South Africa.

One limitation of this research is that the studies were conducted in a controlled lab setting. Thus, one could imagine additional noise from a true consumer setting might increase the effects of these results. Another limitation is the focus on only black and white consumer faces. In this paper, the authors focused on these two races, specifically to keep the factorial design as simplified as possible.

The implications of this research are important given that the ability of employees’ recognizing customer faces can affect customers’ day-to-day interactions in the marketplace.

This study aimed to assess the potential of the Clinical Dementia Rating (CDR) Scale in the prognosis of dementia in elderly subjects.

Dementia staging severity is clinically an essential task, so the authors used machine learning (ML) on the magnetic resonance imaging (MRI) features to locate and study the impact of various MR readings onto the classification of demented and nondemented patients. The authors used cross-sectional MRI data in this study. The designed ML approach established the role of CDR in the prognosis of inflicted and normal patients. Moreover, the pattern analysis indicated CDR as a strong cohort amongst the various attributes, with CDR to have a significant value of p < 0.01. The authors employed 20 ML classifiers.

The mean prediction accuracy varied with the various ML classifier used, with the bagging classifier (random forest as a base estimator) achieving the highest (93.67%). A series of ML analyses demonstrated that the model including the CDR score had better prediction accuracy and other related performance metrics.

The results suggest that the CDR score, a simple clinical measure, can be used in real community settings. It can be used to predict dementia progression with ML modeling.