This research is a creative exploration of transmedia’s ability to offer up a model of distribution and audience engagement for political documentary. Transmedia, as is well known, is a fluid concept. It is not restricted to the activities of the entertainment industry and its principles also reverberate in the practice of political and activist documentary projects. This practice-led research draws on data derived from the production and circulation of Obrero, an independent transmedia documentary. The project explores the conditions and context of the Filipino rebuild workers who migrated to Christchurch, New Zealand after the earthquake in 2011. Obrero began as a film festival documentary that co-exists with two other new media iterations, each reaching its respective target audience: a web documentary, and a Facebook-native documentary. This study argues that relocating the documentary across new media spaces not only expands the narrative but also extends the fieldwork and investigation, forms like-minded publics, and affords the creation of an organised hub of information for researchers, academics and the general public. Treating documentary as research can represent a novel pathway to knowledge generation and the present case study, overall, provides an innovative model for future scholarship.
Mechanistic and scientific approaches to resilience assume that there is a “tipping point” at which a system can no longer absorb adversity; after this point, it is liable to collapse. Some of these perspectives, particularly those stemming from ecology and psychology, recognise that individuals and communities cannot be perpetually resilient without limits. While the resilience paradigm has been imported into the social sciences, the limits to resilience have often been disregarded. This leads to an overestimation of “human resourcefulness” within the resilience paradigm. In policy discourse, practice, and research, resilience seems to be treated as a “limitless” and human quality in which individuals and communities can effectively cope with any hazard at any time, for as long as they want and with any people. We critique these assumptions with reference to the recovery case in Ōtautahi Christchurch, Aotearoa New Zealand following the 2010-11 Canterbury earthquake sequence. We discuss the limits to resilience and reconceptualise resilience thinking for disaster risk reduction and sustainable recovery and development.
Background: Up to 6 years after the 2011 Christchurch earthquakes, approximately one-third of parents in the Christchurch region reported difficulties managing the continuously high levels of distress their children were experiencing. In response, an app named Kākano was co-designed with parents to help them better support their children’s mental health. Objective: The objective of this study was to evaluate the acceptability, feasibility, and effectiveness of Kākano, a mobile parenting app to increase parental confidence in supporting children struggling with their mental health. Methods: A cluster-randomized delayed access controlled trial was carried out in the Christchurch region between July 2019 and January 2020. Parents were recruited through schools and block randomized to receive immediate or delayed access to Kākano. Participants were given access to the Kākano app for 4 weeks and encouraged to use it weekly. Web-based pre- and postintervention measurements were undertaken. Results: A total of 231 participants enrolled in the Kākano trial, with 205 (88.7%) participants completing baseline measures and being randomized (101 in the intervention group and 104 in the delayed access control group). Of these, 41 (20%) provided full outcome data, of which 19 (18.2%) were for delayed access and 21 (20.8%) were for the immediate Kākano intervention. Among those retained in the trial, there was a significant difference in the mean change between groups favoring Kākano in the brief parenting assessment (F1,39=7, P=.012) but not in the Short Warwick-Edinburgh Mental Well-being Scale (F1,39=2.9, P=.099), parenting self-efficacy (F1,39=0.1, P=.805), family cohesion (F1,39=0.4, P=.538), or parenting sense of confidence (F1,40=0.6, P=.457). Waitlisted participants who completed the app after the waitlist period showed similar trends for the outcome measures with significant changes in the brief assessment of parenting and the Short Warwick-Edinburgh Mental Well-being Scale. No relationship between the level of app usage and outcome was found. Although the app was designed with parents, the low rate of completion of the trial was disappointing. Conclusions: Kākano is an app co-designed with parents to help manage their children’s mental health. There was a high rate of attrition, as is often seen in digital health interventions. However, for those who did complete the intervention, there was some indication of improved parental well-being and self-assessed parenting. Preliminary indications from this trial show that Kākano has promising acceptability, feasibility, and effectiveness, but further investigation is warranted. Trial Registration: Australia New Zealand Clinical Trials Registry ACTRN12619001040156; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377824&isReview=true
The rapid classification of building damage states or placards after an earthquake is vital for enabling an efficient emergency response and informed decision-making for rehabilitation and recovery purposes. Traditional methods rely heavily on inspector-led on-site surveys, which are often time-consuming, resource-intensive, and susceptible to human error. This study introduces a machine learning-supported surrogate model designed to streamline the assessment of building damage, focusing on the automated assignment of damage placards within the context of New Zealand's post-earthquake evaluation frameworks. The study evaluates two key safety evaluation protocols—Rapid Building Assessment (RBA) and Detailed Damage Evaluation (DDE)—and integrates corresponding databases derived from the 2010–2011 Canterbury Earthquake Sequence (CES) in Christchurch. Six ML classifiers—Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Gradient Boosting Classifier (GBC), and Gradient Bagging (GBag)—were rigorously tested across both databases. The results indicate that the RF-based surrogate model outperforms the other classifiers across both RBA and DDE protocols. Two distinct sets of critical predictors have been further identified for each protocol, allowing for the rapid retrieval of essential data for future on-site surveys, while retaining the RF model's predictive accuracy. The developed surrogate model provides a pragmatic tool for practising engineers to rapidly assign placards to damaged structures and for policymakers and building owners to make informed recovery decisions for earthquake-affected buildings.
