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This study aims to develop a model to predict non-violent work behaviour (NVWB) among employees using machine learning techniques.

Four machine learning techniques (Naïve Bayes, decision tree, logistic regression and ensemble learning) were used to develop a prediction model for NVWB of employees. Also, 10-fold cross-validation method was used to validate the NVWB prediction models. The confusion matrix is used to derive various performance matrices to express the predictive capability of NVWB models quantitatively.

The model developed using random forest technique was identified as best NVWB prediction model, as it resulted in highest true positive rate and true negative rate, thereby resulting in the highest geometric mean, balance and area under receiver operator characteristics curve.

To the best of the authors’ knowledge, this is one of the pioneer studies that used machine learning techniques to develop a predictive model of NVBW.

Building on the concept of “impact literacy” established in a previous paper from Bayley and Phipps, here we extend the principles of impact literacy in light of further insights into sector practice. More specifically, we focus on three additions needed in response to the sector-wide growth of impact: (1) differential levels of impact literacy; (2) institutional impact literacy and environment for impact; and (3) issues of ethics and values in research impact. This paper invites the sector to consider the relevance of all dimensions in establishing, maintaining and strengthening impact within the research landscape. We explore implications for individual professional development, institutional capacity building and ethical collaboration to maximise societal benefit.

The attempt to establish a common European framework for core platforms' duties and responsibilities toward other actors in the digital environment is at the core of the recent scholarly debate surrounding the Digital Markets Act (DMA) proposal. In particular, the everlasting juxtaposition between the “data power” – as emerging from recent cases (Section 2) – that dominant tech companies enjoy and the concept of consumer sovereignty (Section 3) lies at the core of the proposal's attempt to identify digital core platforms as market gatekeepers. Accordingly, this chapter critically investigates the divide between power imbalance and consumer sovereignty in light of the architecture designed by the DMA, with a specific focus on its effectiveness in identifying gatekeepers' power drivers (Section 4). After highlighting the main critical aspects of the pertinent rules, opportunities for fruitful developments are then identified through the reframing of some of the notions considered in the proposal, and namely the role of “lock-in” effects and “data accumulation” (Section 5). Lastly, this chapter suggests that the DMA advancements – while desirable – are bound to be fragmentary in the absence of a wider appraisal of the nature of data power imbalance dynamics in the modern digital markets (Section 6).

Explore what popular food influencers among Flemish emerging adults portray in their Instagram recipe posts in terms of (1) references to food literacy, (2) nutritional value, (3) rational and emotional appeals and (4) the relation between the nutritional value and rational/emotional appeals.

A content and nutritional analysis of Instagram recipe posts from seven food influencers (N = 166).

Findings reveal that food influencers rarely embed references to food literacy in their recipe posts, especially regarding meal planning, food selection, meal consumption and evaluating food-related information. Only in 28.9% of the posts information was given on how to prepare a recipe. Second, 220 recipes were included in the 166 recipe posts, of which the majority (65%) were main course meals that met at least six of the 11 nutrient criteria for a healthy main meal (67.2%). Finally, food influencers promote their recipe posts as positive narratives, focusing on the tastiness (66%) and convenience (40.9%) of meals.

This is the first study to evaluate what food influencers post nutritionally in their Instagram recipes, as well as how they promote these recipes. Health promotors should note the influential role of food influencers and seek ways to collaborate to provide information on how food literacy cues can be embedded in influencers' communications and provide insights into how influencers' recipes can be optimised.

This paper aims to present new evidence from the Cambridge Study in Delinquent Development (CSDD) showing the extent to which obstetric (e.g. abnormal birth weight, confinement at birth, severe abnormality of pregnancy, etc.) and early childhood and family factors (illegitimate child, unwanted conception, family overcrowding, etc.) have predictive effects on psychopathic traits measured later in life at age 48 years.

Data collected in the CSDD are analysed. This is a prospective longitudinal study of 411 London men from age 8 to age 61 years.

The results suggest that none of the obstetric problems were predictive of adult psychopathy. However, some other early childhood factors were significant. Unwanted conception (by the mother) was significantly associated with high psychopathy. The likelihood of being an unwanted child was higher when the mother was younger (19 years or less), and when the child was illegitimate. The poor health of the mother and living in an overcrowded family were also significant in predicting psychopathy in adulthood, as well as both psychopathic personality (F1) and psychopathic behaviour (F2).

These findings suggest the influence of very early emotional tensions and problematic social background in predicting psychopathic traits in adulthood (at age 48 years). They also emphasise the importance of investigating further the very early roots of psychopathic traits.