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Otherwise than human: responsibility in the wake of disaster, an eco-deconstructive response to climate catastrophe
Get PDFThis thesis will, firstly, assume a line of eco-deconstructive reasoning, which takes as its point of departure from other approaches to the environment the basic premises of deconstruction, which aim to de-center the ontological and epistemological assumptions of Western thought about the subject and language, and apply it to the concept of the human as a distinct and, most importantly, privileged being as this privilege has operated within language and culture, thereby demonstrating how climate catastrophe constitutes a fundamental rupture in traditional ontology. Secondly, I will turn towards the ethics of Emmanuel Levinas, as in present in his God, Death, and Time and Totality and Infinity, and bring the anthropocentrism at the heart of his philosophy to bear the weight of responsibility we owe to, not just our human neighbors but, more significantly, to our nonhuman neighbors at a time when those nonhuman others can no longer go unrecognized
Strategies for Early Learners, Second Edition
Get PDFWelcome to learning about how to effectively plan curriculum for young children. This textbook will address: Developing curriculum through the planning cycle Theories that inform what we know about how children learn and the best ways for teachers to support learning The three components of developmentally appropriate practice Importance and value of play and intentional teaching Different models of curriculum Process of lesson planning (documenting planned experiences for children) Physical, temporal, and social environments that set the stage for children’s learning Appropriate guidance techniques to support children’s behaviors as the self-regulation abilities mature. Planning for preschool-aged children in specific domains including * Physical development * Language and literacy * Math * Science * Creative (the visual and performing arts) * Diversity (social science and history) * Health and safety Making children’s learning visible through documentation and assessmenthttps://scholar.utc.edu/open-textbooks/1005/thumbnail.jp
The Role of Fairness, Transparency, and Feedback in Driving Motivation and Performance Among Employees in Incentive-Based Roles
Get PDFOrganizations are under increasing pressure to design reward systems that are not only competitive but also perceived as fair and transparent by employees. This issue is particularly salient in today’s workforce, where pay transparency legislation and widespread information sharing have heightened employee expectations for openness and equity. Guided by Equity Theory and organizational justice frameworks, the present study is aimed to investigate how pay fairness, reward transparency, and feedback quality interact to shape motivation and job performance, including discretionary effort. A survey of professionals in incentive-driven roles will be conducted, using validated measures of distributive justice, reward transparency, feedback environment, work motivation, and in-role performance. The proposed conceptual model illustrates three hypothesized effects: (H1) Perceived pay fairness will be positively associated with motivation for job performance, incentive-based employees; (H2) Perceived reward transparency will be positively associated with motivation for job performance; and (H3) Feedback quality moderates the relationship between reward transparency and motivation for job performance such that the positive association is stronger at high levels of feedback quality and nonsignificant at low levels of feedback quality. Data will be analyzed through hierarchical regression and moderation techniques, with mediation being explored appropriately. The findings are expected to advance compensation and motivation research by integrating structural features of reward systems with individual motivational outcomes. The findings will also be used to offer practical implications for organizations seeking to retain and engage talent through fair, transparent, and feedback-rich systems
Perceived Value of AI Integration in Education Scale
No full textArtificial Intelligence (AI) tools are rapidly being adopted into education, but user perceptions of their trustworthiness and utility remain unclear. The purpose of this paper is to define and create a construct that can be used to measure the ethical and functional dimensions of AI acceptance in education. Analyses supported a two factor structure with excellent internal consistency and good construct validity. Future research should validate the scale in varied educational settings and explore its links to learning outcomes. Limitations of this scale are discussed
Understanding the Effect of Perceived Supervisor Unavailability on Job Satisfaction, Counterproductive Work Behaviors, and Intent to Quit
No full textThis study aims to examine the influence of supervisor unavailability on employee job satisfaction, counterproductive work behaviors, and intent to quit. It introduces the construct of Perceived Supervisor Unavailability (PSU), defined as the employee\u27s perception that their supervisor is not present, reachable, or responsive during times when support or input is expected or needed. Grounded in Job Demands-Resources and Social Exchange Theory, this study will look at personal and organizational resources as mediators of the PSU-work outcomes relationship. Role clarity acting as personal resource and perceived supervisor and organizational support, acting as organizational resources are expected to reduce the influence of PSU on work outcomes. Data will be collected from both employees and supervisors working on-site and hybrid/remote conditions to examine whether physical presence influences PSU
Modeling the dynamics of user adoption and abandonment for a single product
Get PDFWe introduce a compartmental differential equation model to study the dynamics of user adoption and abandonment for a single product. The model integrates two forms of abandonment: infectious, driven by user interactions, and non-infectious, prompted by external influences. Notably, the infectious abandonment coefficient varies linearly with the number of previous users. We investigate the existence of equilibria of the model and derive the threshold quantity ℛ0. The user-free equilibrium is always present, and its stability is analyzed under the condition ℛ0 \u3c 1. Moreover, a user-prevailing equilibrium does not exist when ℛ0 ≤ 1, but at least one user-prevailing equilibrium is guaranteed when ℛ0 \u3e 1. We further characterize conditions for multiple equilibria and various bifurcations, including saddle-node, -shaped, and Hopf bifurcations, and formulate an optimal control problem. Numerical simulations validate our theoretical findings, and the historical LinkedIn and YouTube data calibrate the model to forecast future user adoption trends
On solving the vertical generalized linear complementarity problem associated with a vertical block P-matrix
Get PDFIn Ebiefung et al. (2017) an algorithm was developed to solve the vertical generalized linear complementarity problem (VGLCP) when the associated matrix is a vertical block P-matrix (VBP). The objective of this study is to implement the algorithm on a large scale using the Python programming language. We also use a Python code to generate test VBP- matrices Ebiefung et al. (2022), and used them to implement the algorithm. On a representative example, every computational step including basis updates, pivot selection, and vector analysis is recorded. The Python implementation exhibits precise convergence. The final results verifying the correctness of the solution are checked against complementarity and feasibility requirements
Fine-Tuning a Domain-Specific Language Model for Truss Structural Analysis
Get PDFThis research investigates the feasibility of fine-tuning a domain-specific language model (LLM) to enhance the accuracy and accessibility of truss structural analysis. General-purpose AI models, such as ChatGPT, often struggle with engineering-specific problems due to insufficient domain knowledge. To address this, we propose fine-tuning a small LLM tailored for structural engineering tasks, with truss analysis using the stiffness method as a case study. The project leverages a curated dataset of textual and diagrammatic inputs from engineering textbooks, manuals, and solved examples. Image-to-text tools will translate structural diagrams into machine-readable formats, reducing data preparation costs while maintaining numerical precision. Fine-tuning involves preprocessing domain-specific datasets, adapting pre-trained models using supervised learning, and incorporating physics-based constraints. A transformer-based model, such as Flan-T5, will be fine-tuned to process text-based truss problems and produce detailed analysis results, including intermediate calculations and force distributions. The model’s accuracy and computational efficiency will be evaluated against benchmarks, conventional analysis tools, and case studies. Although the primary focus is on text-based inputs, this work lays the foundation for integrating visual inputs, providing engineers and students with a practical tool for rapid and accurate analysis. By addressing challenges in dataset preparation, fine-tuning, and evaluation, this study advances the application of AI in structural engineering
Ethical Practices of AI in Performance Management & Employee Development
Get PDFAs artificial intelligence reshapes the landscape of workplace systems, industrial-organizational (I/O) professionals face a pivotal challenge: integrating AI into performance management and employee development without compromising the human values that foster trust, growth, and psychological safety. This session offers a strategic and ethical roadmap for navigating that challenge, emphasizing the irreplaceable role of human judgment, empathy, and dialogue in coaching and leadership. Participants will explore a modular framework for ethical AI use, designed to clarify where automation can enhance decision-making and where human expertise must remain central. Through real-world scenarios, interactive exercises, and cross-disciplinary dialogue, the session equips I/O professionals to lead AI adoption with clarity and courage assuring that technology serves as a compass, not a coach. Key themes include the risks of outsourcing leadership to algorithms, the boundaries of automation in developmental conversations, and the stewardship mandate of I/O professionals to safeguard ethical, human-centered strategy. By bridging research and practice, this session empowers attendees to shape AI-integrated workplaces that honor both innovation and integrity
A question to query LLM as a pipeline replacement in knowledge graph question answering systems
Get PDFKnowledge Graph Question Answering (KGQA) pipelines commonly depend on separate entity and relation predictors before querying the graph, which introduces engineering complexity and costly inference passes over large vocabularies. This thesis presents a drop-in replacement for those modules: a fine-tuned large language model (LLM) that translates a natural-language question directly into an executable SPARQL query. We fine-tune instruction-tuned backbones, Llama-3.1-8B-Instruct and Mistral-7B-Instruct, on paired (question, gold SPARQL) examples, which are formatted through chat templates. As a result, the models can perform single-step query generation. The training and inference pipeline includes a lightweight post-processor that corrects tokenizer-induced spacing artifacts in generated SPARQL, improving exact-match robustness without altering query structure. On a held-out test set, the fine-tuned models achieve 97.9% (Llama) and 94.0% (Mistral) exact-match accuracy for natural-language-to-SPARQL generation, demonstrating that an end-to-end translator can meet or exceed the accuracy of typical multi-module KGQA stacks while substantially simplifying the architecture