Wicked Problems Forum: Undue Donor Influence in Higher Education

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Wicked Problems Forum: Undue Donor Influence in Higher Education

August 2, 2023
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Critical Reflections on Donor Influence and the Role of Critical Communication Pedagogies in the Classroom

The retreat of state funding at public institutions paired with the growing concerns surrounding vulture capitalism that has weaponized philanthropic gift-giving (i.e., distinguished chairs, scholarships and fellowships, academic research centers, faculty lines, campus maintenance) means educators must find ways to teach students about the importance of using their knowledge and skillsets to promote public interests and improve lives. The term vulture capitalism is used here as it relates to donor influence to critique the types of donors (individuals, foundations, and corporations) who use gift-giving to advance conservative, elitist agendas that serve privatized interests at the expense of public interests (Carey, 2019; Mintz, 2019). Vulture capitalism and donor (gift-giving), as a case study, provide instructors and students constructive opportunities to reflect on how hegemonic power operates in and impacts our daily lives. To do so, the article begins by reflecting on a few examples of harmful donor influence to demonstrate how discussions concerning vulture capitalism can stimulate important conversations surrounding power, hegemony, and institutional oppression. It is argued that critical communication pedagogy (CCP) assists instructors who wish to teach students how to discuss issues of power and hegemony in contemporary communication classrooms. CCP offers a pragmatic approach to addressing and examining how power operates through a consideration of language and discourse. This article highlights three major tenets of CCP to propose an in-class activity that stresses the importance of dialogic reflexivity in classroom conversations concerning hegemony, power, and communication.

 

Code of Federal Regulations: Title 34 Education

Blockchain Tokens for Food Traceability

August 1, 2023
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Integrating Blockchain, Smart Contract-Tokens, and IoT to Design a Food Traceability Solution

Mark Kim – Brian Hilton
Info. Systems and Technology, Claremont Graduate University
Zach Burks – Jordan Reyes
Harvest Network, Bakersfield, CA, USA

 

Abstract:  Information asymmetry exists amongst stakeholders in the current food supply chain. Lack of standardization in data format, lack of regulations, and siloed, legacy information systems exasperate the problem. Global agriculture trade is increasing creating a greater need for traceability in the global supply chain. This paper introduces Harvest Network, a theoretical end-to-end, vis a vie “farm-to-fork”, food traceability application integrating the Ethereum blockchain and IoT devices exchanging GS1 message standards. The goal is to create a distributed ledger accessible for all stakeholders in the supply chain. Our design effort creates a basic framework (artefact) for building a prototype or simulation using existing technologies and protocols [1]. The next step is for industry practitioners and researchers to apply AGILE methods for creating working prototypes and advanced projects that bring about greater transparency.

Document Sections
    • I. Introduction
    • II.  New Paradigm for Food Traceability
    • III. Tokenization Overview
    • IV. Smart Contract Design and IoT Interaction
    • V. Web Integration

 

PURCHASE INFORMATION: IEEE Digital Library

 

Code for Fire Protection of Historic Structures

July 31, 2023
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Rodeo

July 31, 2023
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American Federation for Children

July 31, 2023
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Motif and Hypergraph Correlation Clustering

July 30, 2023
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Partial map of the Internet based on the January 15, 2005 data found on opte.org.

 

Motif and Hypergraph Correlation Clustering

Pan Li – Olgica Milenkovic

University of Illinois at Urbana–Champaign

Gregory J. Pule

Auburn University

 

Abstract:  Motivated by applications in social and biological network analysis we introduce a new form of agnostic clustering termed motif correlation clustering, which aims to minimize the cost of clustering errors associated with both edges and higher-order network structures. The problem may be succinctly described as follows:

Given a complete graph G , partition the vertices of the graph so that certain predetermined “important” subgraphs mostly lie within the same cluster, while “less relevant” subgraphs are allowed to lie across clusters. Our contributions are as follows: We first introduce several variants of motif correlation clustering and then show that these clustering problems are NP-hard.

We then proceed to describe polynomial-time clustering algorithms that provide constant approximation guarantees for the problems at hand. Despite following the frequently used LP relaxation and rounding procedure, the algorithms involve a sophisticated and carefully designed neighborhood growing step that combines information about both edges and motifs. We conclude with several examples illustrating the performance of the developed algorithms on synthetic and real networks.

CLICK HERE for complete paper

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