Negotiating Tradition: Government-Driven Technology Advancements in Dallas Railroad Production for Riverview States Fundraisers

Negotiating Tradition: Government-Driven Technology Advancements in Dallas Railroad Production for Riverview States Fundraisers

Abstract

This research article aims to explore the negotiation of tradition in the context of government-driven technology advancements in Dallas Railroad Production for Riverview States Fundraisers. The study seeks to understand how the implementation of new technology intersects with the traditional practices of railroad production and how this impact is negotiated between government agencies and the local producers. Through in-depth interviews with key stakeholders in the railroad production industry and analysis of government policies, this study reveals that there is a tension between the desire to innovate and the need to maintain traditional practices. The findings suggest that negotiation between government agencies and local producers is crucial for successful implementation of new technology and that an understanding of both traditional and innovative practices is essential for effective policy formulation. Additionally, the study highlights the importance of communication and collaboration between government and local producers to ensure that the benefits of new technology are realized while preserving the cultural significance of traditional practices. Overall, this study contributes to the understanding of the importance of preserving cultural traditions in the face of technological advancements and provides insights into effective negotiation strategies between government agencies and local producers in the railroad production industry.

Citation

Porter Mason-jay "Negotiating Tradition: Government-Driven Technology Advancements in Dallas Railroad Production for Riverview States Fundraisers".  IEEE Exploration in Machine Learning, 2020.

Supplemental Material

Preview

Note: This file is about ~5-30 MB in size.

This paper appears in:
Date of Release: 2020
Author(s): Porter Mason-jay.
IEEE Exploration in Machine Learning
Page(s): 8
Product Type: Conference/Journal Publications