Public Information and the Persistence of Bond Market Volatility

We examine the reaction of daily bond prices to the release of government macroeconomic news. These news releases are of interest because they are released on periodic, preannounced dates and because they cause substantial bond market volatility. The news component of volatility is not positively autocorrelated on these dates, since the news is released at a specific moment in time. We find that (1) expected returns on the short end of the bond market are significantly higher on these announcement dates, and (2) the persistence pattern of daily volatility is quite different around these days.

Rearing Bumble Bees for Research and Profit: Practical and Ethical Considerations

The commercial production of bumble bee colonies is a multi-million dollar business worldwide. The pollination of greenhouse tomatoes is largely dependent on this industry. However, microparasites are prevalent in many of these colonies and can spread to wild populations of bumble bees. Academic researchers now commonly purchase colonies for their work. I believe that this raises some questions: (a) What is the danger of exacerbating the problem of spread of parasites and pathogens to wild population of bumble bees from field studies using purchased colonies? (b) How representative studies are done on only a few species, for example, B. terrestris, B. impatiens? (c) Does the purchase and use of these colonies give tacit approval to the industry, which may be having a detrimental effect on the native populations of bumble bees? This is an ethical issue. (d) Loss of “feeling for the organism” by researchers and particularly graduate students. These issues were discussed, and the classical method of bumble bee rearing which avoids these problems was described

Historical archaeology of Alaskan placer gold mining settlements: Evaluating process-pattern relationships

Thesis (Ph.D.) University of Alaska Fairbanks, 1998The objective of this research is to explicate appropriate methods for investigating relationships between past historical processes and variables, and resulting contemporary patterns in archaeological and historical data sets. Turn-of-the-twentieth century placer gold mining in interior Alaska is used as a case study to evaluate these relationships. By linking observable patterns in historical data sets with the variables and processes that in part create and shape them, a more-complete, context-specific explanation of past events and actions emerges when the data are evaluated in specific historical settings. The methodological approach used here is to just formulate explicit "expectations," and then to evaluate them against independent Alaskan historical and archaeological data sets. The expectations derive from independent comparative historical geographical, and archaeological research. One series of nine expectations evaluates attributes of artifacts relating to site and feature abandonment processes relating to curation and scavenging, including specific traits of artifacts in curated and scavenged deposits; the changing effects of continued curation and scavenging on an artifactual assemblage through time; and spatial characteristics of artifacts within curated and scavenged foundations. Four types of data are used evaluate the expectations, including the size of artifacts, whether they are still functional or usable, their spatial provenience within excavated structures, and a feature's data range. Seven of these expectations are corroborated, one is falsified, and one requires further data for a full evaluation. A second series of seven expectations examines aspects of placer gold mining settlement and transportation systems, including the core-peripheral relationship between Alaska and the United States; the nature of expansion of gold mining settlements into new areas; locational, demographic, and physical layout characteristics of settlement systems; the mining settlement hierarchy and its changing components through time; and characteristics of the supporting transportation supply system. These expectations, while also corroborated by the Alaskan data, lend themselves more to historical context-specific understanding and interpretation, as opposed to the strict corroboration-falsification dichotomy of the abandonment analyses

Predicting undergraduate students’ learning from a lecture: The role of self-control, motivation, and mental effort.

For the first time, the present study investigated the combined role of selfcontrol, motivation, and mental effort in predicting undergraduate students’ learning from a two-hour presentation-format higher education lecture. The study comprised 62 students, in a final-year BSc undergraduate Sports Coaching lecture, who completed questionnaires measuring: state self-control during the lecture; mastery approach and performance avoidance motivation towards learning the course topic; mental effort invested during the lecture; and content retention immediately after the lecture (i.e., learning). Moderated mediation analyses revealed that greater levels of state self-control (W variable) were necessary for students to transform their motivation (X variable; mastery approach or performance avoidance) into mental effort (M variable) to benefit their learning (Y variable) during the lecture. Avenues for applied interventions to motivate students and increase their self-control resources within higher education environments are discussed

Psychosocial and Physiological Factors Affecting Selection to Regional Age-Grade Rugby Union Squads: A Machine Learning Approach

Talent selection programmes choose athletes for talent development pathways. Currently, the set of psychosocial variables that determine talent selection in youth Rugby Union are unknown, with the literature almost exclusively focusing on physiological variables. The purpose of this study was to use a novel machine learning approach to identify the physiological and psychosocial models that predict selection to a regional age-grade rugby union team. Age-grade club rugby players (n = 104; age, 15.47 ± 0.80; U16, n = 62; U18, n = 42) were assessed for physiological and psychosocial factors during regional talent selection days. Predictive models (selected vs. non-selected) were created for forwards, backs, and across all players using Bayesian machine learning. The generated physiological models correctly classified 67.55% of all players, 70.09% of forwards, and 62.50% of backs. Greater hand-grip strength, faster 10 m and 40 m sprint, and power were common features for selection. The generated psychosocial models correctly classified 62.26% of all players, 73.66% of forwards, and 60.42% of backs. Reduced burnout, reduced emotional exhaustion, and lower reduced sense of accomplishment, were common features for selection. Selection appears to be predominantly based on greater strength, speed, and power, as well as lower athlete burnout