Month: September 2024

The Multi-Scalar Inequities of Climate Adaptation Finance: A Critical Review

Latest Results

Abstract

Purpose of Review

Following a multi-scalar analytical approach, this critical literature review explores the factors that determine adaptation finance accessibility and allocation with particular attention to how the needs of climate-vulnerable communities are considered.

Recent Findings

Our review reveals that climate vulnerability is not a primary determinant in the accessibility and allocation of climate adaptation finance at inter-state, sub-national and local scales. Instead, factors such as institutional capacities and financial and political interests exert significant influence. This leads to maladaptation and multi-scalar inequities where climate finance favours relatively resilient groups across scales with less support for more vulnerable populations.

Summary

We argue that finance does not trickle down, but “ripples” within a climate finance arena – where we define the latter as a messy space of competition, negotiation and collaboration. To unlock equitable adaptation finance patterns, future research should focus on the multi-scalar configurations of adaptation finance beyond the international level and consider local and regional territorial and scalar politics.

“I wonder if you will be sad?”: Employing the concept of mentalization psychosocially with low-income mothers

Latest Results

Abstract

We demonstrate the value of a rapprochement between psychoanalytic work aimed at supporting marginalized mothers and discursive accounts of low-income mothers, providing a psychosocial analysis of data from an interview study with six low-income South African mothers. Employing discourse analysis, we show how instrumental mothering is a dominant and precarious construction in these mothers’ talk. We use the concept of mentalization to track the affective work that accompanies the interactional emergence of this instrumental mothering discourse in a particular interview encounter. The implications of the research are discussed in the light of increasing demands for sociocultural responsive research and clinical practice.

Exploring cranial macromorphoscopic variation and classification accuracy in a South African sample

Latest Results

Abstract

To date South African forensic anthropologists are only able to successfully apply a metric approach to estimate population affinity when constructing a biological profile from skeletal remains. While a non-metric, or macromorphoscopic approach exists, limited research has been conducted to explore its use in a South African population. This study aimed to explore 17 cranial macromorphoscopic traits to develop improved methodology for the estimation of population affinity among black, white and coloured South Africans and for the method to be compliant with standards of best practice. The trait frequency distributions revealed substantial group variation and overlap, and not a single trait can be considered characteristic of any one population group. Kruskal-Wallis and Dunn’s tests demonstrated significant population differences for 13 of the 17 traits. Random forest modelling was used to develop classification models to assess the reliability and accuracy of the traits in identifying population affinity. Overall, the model including all traits obtained a classification accuracy of 79% when assessing population affinity, which is comparable to current craniometric methods. The variable importance indicates that all the traits contributed some information to the model, with the inferior nasal margin, nasal bone contour, and nasal aperture shape ranked the most useful for classification. Thus, this study validates the use of macromorphoscopic traits in a South African sample, and the population-specific data from this study can potentially be incorporated into forensic casework and skeletal analyses in South Africa to improve population affinity estimates.

Prediction of global trade network evolution with uncertain multi-step time series forecasting method

Latest Results

Abstract

Predicting the evolutionary trends of complex systems is a critical issue in the field of complex system science. Based on the uncertain theory framework, this study proposes an uncertain multi-step time series forecasting aimed at predicting trends of network evolution, and applies it to the prediction of the future development of the global trade network. Specifically, this study utilizes inter-country input–output tables to construct the global trade network based on social network analysis methods. Furthermore, supply-side and demand-side trade dependency indicators are proposed to identify the evolutionary characteristics of the global trade network. The uncertain multi-step time series forecasting is subsequently utilized to predict network evolution in 2025–2035, and the network associations between China and the five regional trading blocs and countries are mainly analyzed. This study broadens the theoretical approaches for predicting the future evolution of complex systems.