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Reflexive Antiracism is an approach to antiracism that seeks to avoid the limitations of essentialism and negative emotional reactions through a focus on racialisation (a concept that encompasses both racism and antiracism) as well as the formation and maintenance of racialised identities. This paper aims to outline the construction and validation of a scale to measure this novel theoretical construct: the Reflexive Antiracism Scale‐Indigenous (RAS‐I).

In the context of a cultural training course focused on Indigenous peoples in Australia, 20 items to assess attitudes were developed along with four hypothetical scenarios designed to assess behavioural intentions in specific situations. The survey formed by these items and scenarios was piloted to assess test‐retest, concurrent and construct validity as well as item endorsement and internal reliability.

Findings suggest that an 11‐item scale based on this survey forms a valid and reliable measure of Reflexive Antiracism. Further research and applications are discussed.

This paper will prompt further exploration of Reflexive Antiracism as a concept that can be applied in a range of settings where a more nuanced understanding and approach to antiracism may be of benefit. Being aware of their position within a society that is racialised will allow antiracists to be reflexive (and realistic) about their ability as individuals to achieve antiracist ideals while continuing to strive towards them.

This study aims to use pre‐treatment assessment scores to predict the drop out of 103 incarcerated male violent perpetrators undertaking a long‐term aggression programme, namely the Cognitive Self Change Programme (CSCP), in six English prisons. A hierarchy of best predictors of attrition in this sample is developed. Results found eight out of the 46 assessment variables analysed had a significant association with treatment drop out. Further to this, discriminant function analysis predicted group membership with 80% accuracy, successfully distinguishing perpetrators who dropped out of the programme from those who completed it. The findings support the use of identifying risk factors pre‐treatment to predict drop out and offer a practical way to identify group members who are likely to drop out of the CSCP in addition to identifying markers for programme improvement. The need for further research to increase our understanding of the underlying causal explanations that link specific assessment items to treatment dropout is discussed.

UNTIL 1952 Queen's University was fortunate to have one main library building. With the establishment of the Institute of Clinical Science in the hospital area 1½ miles from the main university site, the formation of a separate medical library near the hospitals was considered essential.

This work aims to establish the relationship between painting art and sustainability, which allows for highlighting implications likely to improve sustainability for humanity's welfare.

To achieve this objective, painting art is measured by a composite index aggregating the quantity and quality represented by the market value. As for sustainable development, it is represented by a composite index comprising three variables: the climate change performance index (ecological dimension), the wage index reflecting distributive justice (social dimension) and the gross domestic product (economic dimension). The composite indices were determined through adjusted data envelopment analysis. In addition, two other methods are used in this work: correlation analysis and a neural network method. These methods are applied to data from 2007 to 2021 across the world.

The correlation method highlighted a perfect positive correlation between painting art and sustainability. As for the neural network method, it revealed that the quality of painting has the greatest impact on sustainability. The neural network method also showed that the most positively impacted variable of sustainability by painting art is the social variable, with a pseudo-probability of 0.90.

The relationship between painting art and sustainability is underexplored, in particular in terms of statistical analysis. Therefore, this research intends to fill this gap. Moreover, analysis of the relationship between both using composite indices computed via an original method (adjusted data envelopment analysis) and a neural network method is nonexistent, which constitutes the novelty of this work.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0006