Sustainable Economic Development

This book is a pivotal publication that addresses the contemporary challenges of globalization and elaborate policy responses to environmental pollution, climate change, economic disruptions, poverty, hunger, and other threats to sustainable economic development. Many parts of the world, territories, and societies are now changing at an unprecedented pace in ways that fundamentally affect the markets, people, the environment, and biodiversity. Such changes are primarily driven by rapid social and economic developments, economic disparities between countries, the internationalization of production and value chains, and industrialization. Increasingly frequently, business interests are interfering with sustainable development goals. The issue is how to converge the economic benefits with the urgent need for establishing resilient production chains, social networks, sustainably-operating markets, and environmental protection. This publication highlights the need for the balanced economic development and comprehensive coverage of many sustainability–business areas. Economic, production, financial, and social factors of sustainability are discussed by over 90 contributors representing 40 universities and research institutions from seven countries. Their findings are translated into workable approaches and policies for the benefit of the global economy, people, and the environment

Initial Development of the Reasons for Reckless and Destructive Behaviours Inventory: Expanding the Role of Dissociation in Self-Destructive Behaviours

Recent research has conceptualized self-destructive behaviours (SDB; e.g., self-injury) as being performed to serve specific functions; however, few measures exist that examine common functions across a range of SDB types. In addition, although dissociative experiences (e.g., depersonalization) are often endorsed by individuals who engage in SDB, measurement of these experiences as reasons for SDB are rarely assessed. In this thesis, we used a trauma-informed approach to evaluate motivations for SDB by initially developing the Reasons for Reckless and Destructive Behaviours Inventory (RRDI). Basic psychometric statistics of reliability, mean item-endorsement, convergent validity, and construct validity were performed across the scales of the RRDI. In addition, for the RRDI self-injury subsection, we evaluated the factor structure, sex invariance, and examined different profiles of individuals in terms of motivations for self-injury. This study has implications for research pertaining to Posttraumatic Stress Disorder and motivational models of self-injury

IMPACT OF DATA COLLECTION ON ML MODELS: ANALYZING DIFFERENCES OF BIASES BETWEEN LOW- VS. HIGH-SKILLED ANNOTATORS

Labeled data is crucial for the success of machine learning-based artificial intelligence. However, companies often face a choice between collecting few annotations from high- or low-skilled annotators, possibly exhibiting different biases. This study investigates differences in biases between datasets labeled by said annotator groups and their impact on machine learning models. Therefore, we created high- and low-skilled annotated datasets measured the contained biases through entropy and trained different machine learning models to examine bias inheritance effects. Our findings on text sentiment annotations show both groups exhibit a considerable amount of bias in their annotations, although there is a significant difference regarding the error types commonly encountered. Models trained on biased annotations produce significantly different predictions, indicating bias propagation and tend to make more extreme errors than humans. As partial mitigation, we propose and show the efficiency of a hybrid approach where data is labeled by low-skilled and high-skilled workers

Conscious and unconscious: passing judgment

The extent to which conscious and unconscious mental processes contribute to our experiences of learning and the subsequent knowledge has been subject to great debate. Dual process theories of implicit learning and recognition memory bear many resemblances, but there are also important differences. This thesis uses subjective measures of awareness to explore these themes using the artificial grammar learning (AGL) and remember/know (R/K) procedures. Firstly, the relationship between response times associated with intuition and familiarity based responding (conscious judgment of unconscious structural knowledge) compared to rule and recollection based responding (conscious structural knowledge) in AGL were found to be strikingly similar to remembering and knowing; their R/K analogues. However, guessing (unconscious judgment knowledge) was also distinct from intuition and familiarity based responding. Secondly, implicit learning in AGL was shown to occur at test, which would not be expected in R/K. Finally, wider theories of cognition, unconscious thought and verbal overshadowing, were shown to have measurable effects on AGL and R/K respectively. The approach used in this thesis shows the merits of both in-depth analysis within a given method combined with the synthesis of seemingly disparate theories. This thesis has built upon the important distinction between conscious and unconscious structural knowledge but also suggests the conscious-unconscious division for judgment knowledge may be as important. Implicit learning and recognition memory tasks differ in the kinds of mental processes that subjective measures are sensitive toward; particularly so in situations where judgment knowledge is unconscious. Different theories and methods divide nature in different ways; the conscious-unconscious judgment distinction may prove an important one