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Routine Narrative Practice as an Activity to Foster the Development of Children's Narrative Quality

2026-07-01T00:00:00Z

Title: Routine Narrative Practice as an Activity to Foster the Development of Children's Narrative Quality Authors: Brubacher, SP; Westerveld, M; Zekiroski, H; Holder, R; Powell, MB Abstract: The present study investigated whether repeated practice in answering open-ended questions about personal events (narrative practice) benefits the narrative quality of unrelated accounts, and whether it had a rapport-building function. Over 4 sessions, children (N = 86) aged 6–12 (M = 8.71, SD = 1.76; 56% female; 20% Aboriginal) talked with RA1 about personal events (practice condition) or did a craft (control condition). The following week, children watched a film and a new RA asked what happened. Children's narratives were coded for story grammar and accuracy, which were correlated with age. As predicted, the practice condition reported more complex story grammar and more accurate details than the craft condition, with a small increase in errors to follow-up questions, and more frequently endorsed RA1 as a potential disclosure recipient. The results suggest that engaging in ongoing practice responding to open-ended questions about experienced events improves children's narrative abilities and builds rapport.

Tapestry of Tales: Women’s Collative Writings as Legal Storytelling

2026-01-01T00:00:00Z

Title: Tapestry of Tales: Women’s Collative Writings as Legal Storytelling Authors: Poole, K Abstract: This article presents an exploration of the storytelling as a feminist practice generating a counternarrative to law, with particular focus on narrative forms that center the collation of women’s stories, examining the practice from the 1400s to contemporary microbiographies on TikTok. I focus on the literary mode of biographical collection. In doing so, I assert that it is the weaving and compounding of multitudes of stories that give rise to the challenging of law’s normative, masculinized narratives. Seemingly inconsequential, isolated biographical narratives are given larger social meaning when read together.

The paradox of powerful children: children as subjects of social fear and instigators of social change in The Girl with All the Gifts

2026-01-01T00:00:00Z

Title: The paradox of powerful children: children as subjects of social fear and instigators of social change in The Girl with All the Gifts Authors: Poole, K Abstract: Stories often reflect an adult desire to limit child political participation at tension with the hope of their societal liberation. By reading such stories, we can explore the ways in which children are transgressive, and the precise anxieties associated with children as a problematized subject. Using The Girl with All the Gifts, a novel by MR Carey, this article examines the child zombie figure and its associated metaphors of the failed paternalist State, the child as a resource for adult exploitation, and the child as an instigator of radical social change. In doing so, I explore how the monstrous child-zombie may be read through a politicized lens to better recognize our preconceived ideas of children.

Net Load Forecasting for Prosumers Using a Comparative and Ensemble Approach with Feature Selection

2026-01-01T00:00:00Z

Title: Net Load Forecasting for Prosumers Using a Comparative and Ensemble Approach with Feature Selection Authors: Fayez, S; Uddin, MJ; Al Ahsan, S; Kabir, MMJ Abstract: Accurate prediction of net short term energy demand for prosumers is crucial for proper energy management, especially in cases where both energy consumption and production is involved. The energy imbalances caused by the integration of prosumer data into the grid are one of the major reasons behind the instability of the power grid. This study addresses the issue by deploying several machine learning models to predict the net load for prosumers and the impact of feature selection on prediction accuracy. To ensure reliability, the dataset was carefully preprocessed, as it includes different types of temporal, geographical, and weather features. Linear Regression and Gradient Boosting Models like XGBoost, LightGBM, CatBoost, and stacking regression-based ensemble of the GBM were used, and their performance was compared. The stacking regressionbased ensemble model performed the best with an R² of 0.96 without feature selection and 0.95 with feature selection, which also suggests that feature selection has minimal impact on the results while reducing the dimensionality of the data.