Navigating the Challenges of Student Identification in Record Libraries: Insights from an Incumbent Collection Manager Against Unions and Office Recommendations in February

Navigating the Challenges of Student Identification in Record Libraries: Insights from an Incumbent Collection Manager Against Unions and Office Recommendations in February


Download Paper
Download Bibtex


Authors

  • Surien Chaitanya

Related Links


Related Links

Abstract

This paper explores the challenges faced by record libraries in student identification and offers insights into effective management strategies based on the experiences of an incumbent collection manager. The study highlights the inadequacies of relying solely on recommendations from unions and office administrators in identifying students and argues for the need to adopt a more holistic approach that accounts for the diverse needs and expectations of students. Drawing on a range of data sources including interviews with library staff, records management policies, and student feedback, the study provides a nuanced analysis of the difficulties encountered in student identification and the potential solutions that can help address these challenges. The findings suggest that successful student identification requires a multifaceted approach that takes into account organizational culture, staff training and development, technology infrastructure, and communication channels. The paper concludes by highlighting the importance of collaboration and knowledge sharing among library stakeholders in promoting effective student identification practices and ensuring the long-term sustainability of record libraries.

Citation

Surien Chaitanya "Navigating the Challenges of Student Identification in Record Libraries: Insights from an Incumbent Collection Manager Against Unions and Office Recommendations in February".  IEEE Exploration in Machine Learning, 2023.

Supplemental Material

Preview

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

This paper appears in:
Date of Release: 2023
Author(s): Surien Chaitanya.
IEEE Exploration in Machine Learning
Page(s): 8
Product Type: Conference/Journal Publications