Re-Purposing the Physical Space of an Academic Library in the Digital Era: A Case Study of Jawaharlal Nehru University and Delhi University, New Delhi (India)
Bibliographic record
Abstract
Objectives – The issue/return of books and other reading materials available on shelves in academic libraries has declined. Round-the-clock availability of information resources, high-speed internet, cheaper cost of data download, laptops, and smartphones are some of the reasons behind decreased usage of print reading materials. Users are spending more time studying, accessing e-resources, socializing with friends, and discussing with peer groups in the library building. Libraries in developed countries have already recognized the need of the hour and redesigned their spaces to create a variety of reading spaces, creative spaces, quiet spaces, and so forth in the existing building. But the libraries of the higher education institutions in India have not considered the users' needs concerning the library space. No such study has been conducted at large universities in India. This study was conducted with library users of the central library of two large universities located in New Delhi to find users' opinions and views to re-purpose the library space to meet users' needs. Methods – The survey method has been used to understand the use of the existing format of knowledge resources and the need of library users. The central point of the research questionnaire is to optimize the library space of the central libraries of the universities under study. Printed questionnaires were distributed to the users present in the reading halls of the libraries at Jawaharlal Nehru University (JNU), New Delhi, and Delhi University (DU), New Delhi, and online questionnaires were distributed to faculty members. The collected data were analyzed with Microsoft Excel, and various hypotheses were tested using non-parametric tests such as the sign test, Mann-Whitney U test, and Kruskal-Wallis test. Results – Students and research scholars visit daily and spend more than four hours in the library building. They use their time in study, accessing e-resources in the reading halls of the library more than any other place. The users opined the reading halls should have wi-fi facilities, a power source to charge laptops and mobile phones, washrooms near every reading hall, a quiet reading space, and a clean ambient environment for long hours of study in the library building. Conclusion – Library users in the 21st century want more physical space to study, concentrate, socialize, and learn in the informal learning environment. They need library space with the latest infrastructure to connect to the digital world to retrieve study materials and print copies of study materials. Educational institutions should create more varied reading spaces for serious reading, pleasure reading, in-depth reading, and interactive learning spaces in new library buildings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".