Kritische Anmerkungen zur Entwicklung der Gesamtschule in Österreich

Ohne Zusammenfassung     

Efficacy of manual versus free-weight training to improve maximal strength and performance for microgravity conditions

We tested a simple and compact device designed for manual resistance training in conditions of microgravity (Self-Powered Rope Trainer Duo (SPoRT Duo)) to increase muscle performance. Twenty-four participants (20.8 ± 2.1 years) were randomly assigned to a manual resistance group (n = 12) and a free-weight group (n = 12). Participants performed eight exercises (three sets; 8–12 efforts) either with free weights or the SPoRT Duo twice a week for 6 weeks. Maximal isometric force of trunk flexion, back extension and chest press increased (P at least 0.01, d at least 0.52) both in the manual resistance group (18.4% ± 15.0%; 32.7% ± 22.7%; 15.3% ± 9.7%) and free-weight group (18.0% ± 13.9%; 26.6% ± 28.9%; 13.3% ± 7.6%). The change in maximal isometric force of wide grip row in both groups (d at best 0.38) did not reach statistical significance (P at best 0.08). The squat one-repetition-maximum increased in the manual resistance group (29.8% ± 22.1%) and the free-weight group (32.4% ± 26.6%). Jump height, determined by a jump-and-reach test, increased in the free-weight group (9.8% ± 13.2%) but not in the manual resistance group (2.0% ± 8.5%). Manual resistance training was equally effective in increasing strength as traditional resistance training with free weights. This apparatus is a useful addition to current in-flight exercise systems.     

Perfectionism and training distress in junior athletes: a longitudinal investigation

Perfectionistic athletes may train harder and for longer than non-perfectionistic athletes, leaving them susceptible to elevated levels of training distress. So far, however, no study has investigated the relationships between perfectionism and training distress, a key indicator of overtraining syndrome. Furthermore, no study has determined psychological predictors of overtraining syndrome. Using a two-wave design, the present study examined perfectionistic strivings, perfectionistic concerns and training distress in 141 junior athletes (mean age = 17.3 years, range = 16–19 years) over 3 months of active training. Multiple regression analyses were employed to test cross-sectional and longitudinal relationships between perfectionism and training distress. In all analyses, perfectionism emerged as a significant predictor, but strivings and concerns showed differential relationships. When the cross-sectional relationships were regarded, perfectionistic concerns positively predicted training distress (P < .001), whereas perfectionistic strivings negatively predicted training distress (P < .01). When the longitudinal relationships were regarded, only perfectionistic concerns predicted increases in training distress (P < .05), whereas perfectionistic strivings did not (P > .05). The findings suggest that sports scientists who wish to identify athletes at risk of overtraining syndrome may monitor athletes’ perfectionistic concerns as a possible risk factor.     

What goes before the CART? Introducing classification trees with Arbor and CODAP

This volume is largely about nontraditional data; this paper is about a nontraditional visualization: classification trees. Using trees with data will be new to many students, so rather than beginning with a computer algorithm that produces optimal trees, we suggest that students first construct their own trees, one node at a time, to explore how they work, and how well. This build-it-yourself process is more transparent than using algorithms such as CART; we believe it will help students not only understand the fundamentals of trees, but also better understand tree-building algorithms when they do encounter them. And because classification is an important task in machine learning, a good foundation in trees can prepare students to better understand that emerging and important field. We also describe a free online tool—Arbor—that students can use to do this, and note some implications for instruction.     

The emergence of openness: How and why firms adopt selective revealing in open innovation

Open innovation is often facilitated by strong intellectual property rights (IPRs), but it may also function, and even be boosted, when firms deliberately waive some of their IPRs. Extant literature has pointed out the potential benefits of such behavior, but falls short of explaining what triggers firms to practice it in the first place and to maintain or extend it. Since the waiving of IPRs runs counter to common views on strategy and competition and to engrained practices, this is a non-trivial question. To address it, we conduct an empirical study in a segment of the computer component industry which traditionally has taken a rather proprietary stance. With the advent of the open source operating system Linux, firms increasingly waived their IPRs on software drivers. We trace and analyze this process using both qualitative and quantitative methods. Our results indicate that component makers went through a learning process, which led some to realize how selectively waiving IPRs may be beneficial for their business. We uncover customer demand pull as the initial trigger and observe how a positive feedback loop sets in subsequently, leading to a further increase in the use of selective revealing. Overall, we find that openness develops into a new dimension of competition. We discuss the implication of our findings for research on open innovation and highlight how they impact managers in practice.