The entanglement of trust and knowledge on the Web

In this paper I use philosophical accounts on the relationship between trust and knowledge in science to apprehend this relationship on the Web. I argue that trust and knowledge are fundamentally entangled in our epistemic practices. Yet despite this fundamental entanglement, we do not trust blindly. Instead we make use of knowledge to rationally place or withdraw trust. We use knowledge about the sources of epistemic content as well as general background knowledge to assess epistemic claims. Hence, although we may have a default to trust, we remain and should remain epistemically vigilant; we look out and need to look out for signs of insincerity and dishonesty in our attempts to know. A fundamental requirement for such vigilance is transparency: in order to critically assess epistemic agents, content and processes, we need to be able to access and address them. On the Web, this request for transparency becomes particularly pressing if (a) trust is placed in unknown human epistemic agents and (b) if it is placed in non-human agents, such as algorithms. I give examples of the entanglement between knowledge and trust on the Web and draw conclusions about the forms of transparency needed in such systems to support epistemically vigilant behaviour, which empowers users to become responsible and accountable knowers.     

A bioecological perspective on talent identification in junior-elite soccer: A Pan-European perspective

ABSTRACT

Elite soccer clubs across Europe spend ever-increasing sums of money on transfers and salaries for world-class players. Consequently, clubs’ talent identification and development processes for junior players have become more professionalised. Based on a holistic ecological approach, this study presents an analysis of talent identification practices across some of the most productive soccer academies in Europe (N = 11). Data were collected via semi-structured interviews with 11 heads of academy recruitment from clubs in the “big five” European leagues. Clubs were purposively sampled based on their player productivity ranking. Interviews ranged from 52:26 minutes to 114:06 minutes in length (m = 87:53 ± 20.10 minutes). This study argues that holistic ecological approaches the environments were characterised through the interplay of factors that ranged from high-level internal to international level relationships. This resulted in the identification and recruitment of players from local and international environments. The purpose of recruitment was suggested to have a dual purpose: recruitment of players for the first team; recruitment of players for further development/monitoring and/or selling to another club.     

Two-dimensional ferromagnetic superlattices

Mechanically exfoliated two-dimensional ferromagnetic materials (2D FMs) possess long-range ferromagnetic order and topologically nontrivial skyrmions in few layers. However, because of the dimensionality effect, such few-layer systems usually exhibit much lower Curie temperature (T_C) compared to their bulk counterparts. It is therefore of great interest to explore effective approaches to enhance their T_C, particularly in wafer-scale for practical applications. Here, we report an interfacial proximity-induced high-T_C 2D FM Fe₃GeTe₂ (FGT) via A-type antiferromagnetic material CrSb (CS) which strongly couples to FGT. A superlattice structure of (FGT/CS)_n, where n stands for the period of FGT/CS heterostructure, has been successfully produced with sharp interfaces by molecular-beam epitaxy on 2-inch wafers. By performing elemental specific X-ray magnetic circular dichroism (XMCD) measurements, we have unequivocally discovered that T_C of 4-layer Fe₃GeTe₂ can be significantly enhanced from 140 K to 230 K because of the interfacial ferromagnetic coupling. Meanwhile, an inverse proximity effect occurs in the FGT/CS interface, driving the interfacial antiferromagnetic CrSb into a ferrimagnetic state as evidenced by double-switching behavior in hysteresis loops and the XMCD spectra. Density functional theory calculations show that the Fe-Te/Cr-Sb interface is strongly FM coupled and doping of the spin-polarized electrons by the interfacial Cr layer gives rise to the T_C enhancement of the Fe₃GeTe₂ films, in accordance with our XMCD measurements. Strikingly, by introducing rich Fe in a 4-layer FGT/CS superlattice, T_C can be further enhanced to near room temperature. Our results provide a feasible approach for enhancing the magnetic order of few-layer 2D FMs in wafer-scale and render opportunities for realizing realistic ultra-thin spintronic devices.     

全球知识经济中的高等教育

许多国家的政府政策和学术工作都认同在世界范围的知识经济中,高等教育和研究的作用与日俱增,高等教育和知识同时具有全球性、国家性和地方性的特点。高等教育由国家政府各个部门共同孕育,而且其模式受到社会投资方式的影响,跨境或者全球层面的高等教育活动逐渐增加,尤其是对于知识而言。通过研究变化中的全球高等教育图景。可以为政府和国家政策、大学发展战略及国际机构提供建议。     

Effect of cadence selection on peak power and time of power production in elite BMX riders: A laboratory based study

ABSTRACT

The aims of this study were to analyse the optimal cadence for peak power production and time to peak power in bicycle motocross (BMX) riders. Six male elite BMX riders volunteered for the study. Each rider completed 3 maximal sprints at a cadence of 80, 100, 120 and 140 revs · min^?1 on a laboratory Schoberer Rad Messtechnik (SRM) cycle ergometer in isokinetic mode. The riders’ mean values for peak power and time of power production in all 3 tests were recorded. The BMX riders produced peak power (1105 ± 139 W) at 100 revs · min^?1 with lower peak power produced at 80 revs · min^?1 (1060 ± 69 W, (F(2,15) = 3.162; P = .266; η² = 0.960), 120 revs · min^?1 (1077 ± 141 W, (F(2,15) = 4.348; P = .203; η² = 0.970) and 140 revs · min^?1 (1046 ± 175 W, (F(2,15) = 12.350; P = 0.077; η² = 0.989). The shortest time to power production was attained at 120 revs · min^?1 in 2.5 ± 1.07 s. Whilst a cadence of 80 revs · min^?1 (3.5 ± 0.8 s, (F(2,15) = 2.667; P = .284; η² = 0.800) 100 revs · min^?1 (3.00 ± 1.13 s, (F(2,15) = 24.832; P = .039; η² = 0.974) and 140 revs · min^?1 (3.50 ± 0.88 s, (F(2,15) = 44.167; P = .006; η² = 0.967)) all recorded a longer time to peak power production. The results indicate that the optimal cadence for producing peak power output and reducing the time to peak power output are attained at comparatively low cadences for sprint cycling events. These findings could potentially inform strength and conditioning training to maximise dynamic force production and enable coaches to select optimal gear ratios.     

Kinematics and kinetics of the bench-press and bench-pull exercises in a strength-trained sporting population

Understanding how loading affects power production in resistance training is a key step in identifying the most optimal way of training muscular power – an essential trait in most sporting movements. Twelve elite male sailors with extensive strength-training experience participated in a comparison of kinematics and kinetics from the upper body musculature, with upper body push (bench press) and pull (bench pull) movements performed across loads of 10–100% of one repetition maximum (1RM). 1RM strength and force were shown to be greater in the bench press, while velocity and power outputs were greater for the bench pull across the range of loads. While power output was at a similar level for the two movements at a low load (10% 1RM), significantly greater power outputs were observed for the bench pull in comparison to the bench press with increased load. Power output (P _max) was maximized at higher relative loads for both mean and peak power in the bench pull (78.6 ± 5.7% and 70.4 ± 5.4% of 1RM) compared to the bench press (53.3 ± 1.7% and 49.7 ± 4.4% of 1RM). Findings can most likely be attributed to differences in muscle architecture, which may have training implications for these muscles.     

Using causal models to bridge the divide between big data and educational theory

An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.

Practitioner notes

What is already known about this topic

• ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
• Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
• Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.

What this paper adds

• An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
• A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
• An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.

Implications for practice and/or policy

• Causal models can help us to explicitly specify educational theories in a testable format.
• It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
• Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.