Since the early 2020s, the global proliferation of AI platforms has accelerated the rise of ‘human-in-the-loop’ authorship practices, including for writers for performance. Yet are human-AI playwriting experiments a mere gimmick? An unethical misstep? Or a new form of ‘creativity amplification’? In late 2025, I interviewed six Australian performance writers who had experimented with an AI platform in the past two years, in order to gauge their early thoughts on this recent phenomenon. For some, the ethical, political, and authorial ramifications of these tools became too problematic to justify their ongoing use. For others, targeted methods such as role play, semantic searches, and prompt engineering yielded both desired and surprising results, including the potential to subvert the colonial and capitalist logic of AI tools from within. This article outlines how six Australian performance writers have been investigating these possibilities, and how experimenting with AI tools has transformed how they perceive their ongoing creative practice.

Modern animal breeding technologies combining statistics and genomics can provide tools for generating reliable genetic improvement in a wide range of traits but have not yet been applied to honey bees in Australia. Genetic improvement of honey bees will allow the continual identification and use of queens that will permanently and continuously improve traits that are important to the honey bee industry (such as pest and disease resistance, honey production, pollination performance, and temperament). Using genomic data within the context of a honey bee breeding program can identify genetic relationships between individuals to increase accuracy of breeding values. Genomic data can also provide information about managed honey bee populations, to estimate stratification and introgression levels and to understand complex population admixture events or identify signatures of natural and artificial selection. For the majority of livestock species genomic data are obtained through the use of commercially available Single Nucleotide Polymorphism (SNP) array chips, which can provide accurate and evenly distributed SNPs with known locations across the genome. Such chips have only recently become commercial for bees. SNP genotyping can be easily reproducible across different batches of samples and is tolerant of lower quality DNA extracts. In this study, we investigate using DNA extracted from pooled drone samples and imputed genotypes from low pass sequencing for genomic prediction and population structure analysis.

Genetic evaluation of animals with different population ancestry needs to deal with differences in their founder populations or breeds. To accommodate these differences allele frequencies from different genetic groups can be used to correct the genomic relationship matrix to reduce breed and group structure. The majority of the Australian sheep population is comprised of animals originating from 11 major breeds. However, smaller breeds are also used to create crossbred animals for different breeding objectives. To properly include these animals into a multibreed genetic evaluation, the allele frequencies of smaller and often missing breeds need to be included into the allele frequencies of the wider population. This work investigated the use of genotypes from smaller breeds and crossbred populations that are currently not included in the Australian national genetic evaluation (LAMBPLAN) because reference populations for imputation and inclusion in breed adjusted genomic relationship matrices have not been established. Genotypes from 50,999 animals originating from fifteen numerically smaller meat, wool and maternal sheep breeds were combined in a genetic analysis to investigate population structure and diversity within and across breeds and define breed composition. Results are expected to increase the number of animals included in the genetic evaluation and improve prediction accuracy in Australian crossbred populations.

Fear of crime in intercity bus terminals is a critical transportation concern that mainly influences public transportation use, mode choices, and overall satisfaction with travel. This paper seeks to assess perceived environmental features predicting fear of crime among travelers in the case of Karandish intercity bus terminal in Shiraz, Iran. In this study, a questionnaire survey was carried out, in which travelers were asked to state their perceptions on several variables including informal social control, terminal police presence, observed incivilities, noise pollution, air pollution, littering, furniture condition, and pedestrian safety against bus traffic. Then, two ordered logit models were conducted to recognize the factors that affect travelers’ fear of crime. The results showed that perceived informal social control was strongly associated with lower levels of fear of crime among travelers. Perceptions of noise pollution and unpleasant littering in the terminal were significant environmental features as well. Also, observing more incivilities was found to make travelers feel more fearful of crime. Based on our findings, providing natural surveillance is the best solution for reducing the fear of crime in intercity bus terminals.