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The voices of children and young people have been largely neglected in discussions of the extent to which the internet takes into account their needs and concerns. This paper aims to highlight young people’s lived experiences of being online.

Results are drawn from the UnBias project’s youth led discussions, “Youth Juries” with young people predominantly aged between 13 and 17 years.

Whilst the young people are able to use their agency online in some circumstances, many often experience feelings of disempowerment and resignation, particularly in relation to the terms and conditions and user agreements that are ubiquitous to digital technologies, social media platforms and other websites.

Although changes are afoot as part of the General Data Protection Regulation (herein the GDPR) to simplify the terms and conditions of online platforms (European Union, 2016), it offers little practical guidance on how it should be implemented to children. The voices and opinions of children and young people are put forward as suggestions for how the “clear communication to data subjects” required by Article 12 of the GDPR in particular should be implemented, for example, recommendations about how terms and conditions can be made more accessible.

Children and young people are an often overlooked demographic of online users. This paper argues for the importance of this group being involved in any changes that may affect them, by putting forward recommendations from the children and young people themselves.

The purpose of this paper is to contribute to the empirical evidence about crop yield distributions that are often used in practical models evaluating crop yield risk and insurance. Additionally, a simulation approach is used to compare the performance of alternative specifications when the underlying form is not known, to identify implications for the choice of parameterization of yield distributions in modeling contexts.

Using a unique high-quality farm-level corn yield data set, commonly used parametric, semi-parametric, and non-parametric distributions are examined against widely used in-sample goodness-of-fit (GOF) measures. Then, a simulation framework is used to assess the out-of-sample characteristics by using known distributions to generate samples that are assessed in an insurance valuation context under alternative specifications of the yield distribution.

Bias and efficiency trade-offs are identified for both in- and out-of-sample contexts, including a simple insurance rating application. Use of GOF measures in small samples can lead to inappropriate selection of candidate distributions that perform poorly in straightforward economic applications. The β distribution consistently overstates rates even when fitted to data generated from a β distribution, while the Weibull consistently understates rates; though small sample features slightly favor Weibull. The TCMN and kernel density estimators are least biased in-sample, but can perform very badly out-of-sample due to overfitting issues. The TCMN performs reasonably well across sample sizes and initial conditions.

Economic applications should consider the consequence of bias vs efficiency in the selection of characterizations of yield risk. Parsimonious specifications often outperform more complex characterizations of yield distributions in small sample settings, and in cases where more demanding uses of extreme-event probabilities are required.

The study helps provide guidance on the selection of distributions used to characterize yield risk and provides an extensive empirical demonstration of yield risk measures across a high-quality set of actual farm experiences. The out-of-sample examination provides evidence of the impact of sample size, underlying variability, and region of the probability measure used on the performance of candidate distributions.

The purpose of this paper is to examine the effects of reconciliation and retaliation story endings on subsequent aggressive affect and behavior.

Participants took part in two ostensibly unrelated studies. The first involved reading a violent story, attributed to a biblical or secular source, which ended in either brutal retaliation or peaceful reconciliation. They then took part in a second study in which they completed measures of aggressive affect and behavior.

Participants told that their stories came from a secular source experienced a more aggressive affect than those told that their stories came from a biblical source. In terms of behavioral aggression, a significant difference in effect of the story ending on males and females emerged. Females who read the reconciliation ending had lower levels of behavioral aggression than females who read the retaliation ending. Conversely, males who read the reconciliation ending had higher levels of behavioral aggression than males who read the retaliation ending.

These findings suggest that media depictions of prosocial reactions to unprovoked aggression may not reduce aggression in men.

Results are discussed in terms of moral values espoused by women and men and suggest that anti-violence messages may be strengthened to the extent they address the values important to both.

This study extends research on violent media exposure to a burgeoning literature on reading violent content.

E‐Teaching as the use of information and communication technology (ICT) in education is of growing importance for educational theory and practice. Many universities and other higher education institutions use ICT to support teaching. However, there are contradicting opinions about the value and outcome of e‐teaching. This paper starts with a review of the literature on e‐teaching and uses this as a basis for distilling success factors for e‐teaching. It then discusses the case study of an e‐voting system used for giving student feedback and marking student presentations. The case study is critically discussed in the light of the success factors developed earlier. The conclusion is that e‐teaching, in order to be successful, should be embedded in the organisational and individual teaching philosophy.

The purpose of this paper is to examine factors affecting the use of forage index insurance. Forage is a difficult crop to insure, and index insurance may be well suited for forage insurance and has been implemented in several countries, including Canada, the USA and France. Despite being a promising risk management tool, forage index insurance participation rates in Canada, and other countries are low relative to crop insurance participation rates for grain and oilseed producers.

A survey was conducted with 87 beef and cattle producers from Alberta and Saskatchewan, Canada. A probit regression model was used, and a number of variables were included to examine the use of forage index insurance.

In total, 6 of 11 variables in the model are found to be statistically significant in explaining forage producers’ use of forage index insurance. Results suggest that producers who maintain lower feed reserves are more likely to purchase forage index insurance. Also, producers with higher levels of knowledge of crop insurance and a more positive attitude toward forage insurance are more likely to use forage index insurance. Furthermore, producers are more likely to use forage index insurance if they perceive drought and weather risk as being of greater importance, and if they are younger. The importance of the variable forage index insurance premium price was statistically insignificant. This could be due to the effect of subsidization, reducing the importance of price for the decision to purchase. Similarly, the use of other subsidized risk management policies, including a whole-farm margin policy (e.g. the government program and AgriStability), did not reduce forage index insurance use. A possible explanation for this is that the subsidization of the policies may make it profitable to purchase both, despite the overlapping coverage.

These results may be useful for policy makers interested in increasing forage index insurance participation rates, as forage index insurance participation rates have historically been low relative to grain and oilseed producers.

This study is believed to be one of the first studies regarding the use of forage index insurance by forage producers. Producers can be exposed to catastrophic risks such as drought or other extreme weather events, and forage index insurance may be an effective means to manage these risks. Index insurance determines payments using an index that is correlated to producers’ actual yields. A downside of this method is basis risk, which is the mismatch between the insured index and the producer’s actual yield. Research has focused on basis risk and developing improved methods to reduce basis risk. However, less research has investigated the other important factors that may contribute to forage index insurance use. Producers may have a different risk management environment regarding forage production compared to other farm activities, and these differences have largely not been examined.

This paper aims to report on findings of a cross‐sector empirical study of the sources and drivers of supply chain vulnerability.

The research was undertaken in accordance with the realist tradition. It begins with a descriptive exploratory stage involving an in‐depth exploratory case study of aerospace industry supply chains, validated through in‐depth interviews with managers representing other “critical sectors” of the UK economy. This is followed by an explanatory theoretical stage. The work is supported throughout with reference to relevant literature sources.

The findings highlight the absence of any widespread understanding of the scope of and dynamic nature of the problem, which should be considered from multiple perspectives and at four levels of analysis: value stream/product or process; asset and infrastructure dependencies; organisations and inter‐organisational networks; and social and natural environment.

The paper is normative rather than positive, so focuses on understanding why supply chains are vulnerable to disruption, rather than presenting itself as a prescription for management. The paper does not investigate academic definitions or existing taxonomies of risk.

The work provides some useful insights for practising managers and policy makers.

The paper reports on empirical research, then draws as appropriate on network theory and complex systems perspectives to produce a conceptual model of a supply chain as in interactive adaptive system.

Often, it is assumed that teams are better at solving problems than individuals working independently. However, recent work in engineering, design and psychology contradicts this assumption. This study aims to examine the behavior of teams engaged in data science competitions. Crowdsourced competitions have seen increased use for software development and data science, and platforms often encourage teamwork between participants.

We specifically examine the teams participating in data science competitions hosted by Kaggle. We analyze the data provided by Kaggle to compare the effect of team size and interaction frequency on team performance. We also contextualize these results through a semantic analysis.

This work demonstrates that groups of individuals working independently may outperform interacting teams on average, but that small, interacting teams are more likely to win competitions. The semantic analysis revealed differences in forum participation, verb usage and pronoun usage when comparing top- and bottom-performing teams.

These results reveal a perplexing tension that must be explored further: true teams may experience better performance with higher cohesion, but nominal teams may perform even better on average with essentially no cohesion. Limitations of this research include not factoring in team member experience level and reliance on extant data.

These results are potentially of use to designers of crowdsourced data science competitions as well as managers and contributors to distributed software development projects.

The purpose of this study is to estimate the amount of cash flow deficit, if any, needed to maintain the operating costs and service debt of a startup cow–calf enterprise. The study compares long-term profitability and risk between starting small and building a herd to full carrying capacity or by starting at desired herd capacity.

A dynamic cattle growth model was developed to capture expanding and maintaining the desired herd size. Discounted cash flow (DCF) models over a 15-year period were calculated to estimate net present value (NPV), modified internal rate of return (MIRR) and cash flow deficit to keep the business operating and service debt. Simulation analyses were conducted considering price and production risk.

Starting at the desired herd size was preferred, according to NPV/MIRR and cash flow deficit, but the differences were not substantial. Assuming the operation is liquidated at book values, there was a 36.3% probability of this enterprise having a zero or positive NPV. If the conservative terminal value assumption is relaxed up to feasible market values, the cow–calf enterprise is economically attractive at an estimated 2.4% opportunity cost of capital. However, the producer would experience a cash flow deficit during the first seven years, which was simulated to be $14,892 and $15,985 annual for both strategies.

Innovative methods used in this study include varying the annual opportunity cost of capital as a function of financing decisions, stochastic prices by cattle type and stochastic weaning weights that are a function of a dynamic cattle model.