Many Indian PhD Students Lack Motivation and Skills to Use Academic Journal Articles, Their Libraries Lack Resources and Standards
Bibliographic record
Abstract
A Review of: Saxena, S. (2018). Factors impacting the usage of academic journal articles by PhD students in India. Information Discovery and Delivery, 46(4), 204-213. https://doi.org/10.1108/IDD-09-2017-0069 Abstract Objective – To investigate the factors influencing the use of academic journals by PhD students in India. Design – Grounded analysis. Setting – Five universities in India. Subjects – 147 PhD students. Methods – Subjects were selected using a mix of convenience and purposeful sampling. Email was then used to send the questions, receive the responses, and seek clarification as required. This process was conducted between September 2016 and January 2017. Main results – Completed responses were received from 134 students, resulting in a response rate of approximately 91%. The researcher identified five factors influencing academic journal usage: institutional, task complexity, relevance and application, information quality, and technical. There was “marked” dissatisfaction with library facilities and access to academic resources, with one respondent stating that their library “does not subscribe to a single electronic journal” (p. 209). Other identified issues include students’ insufficient awareness of what is available, limited motivation to “undertake serious research work” (p. 210) and inadequate skill levels to use available resources effectively. Conclusion – Universities should provide the required resources (both human and infrastructure) to ensure their academic libraries meet quality standards. To do so requires appropriate funding. Additionally, researchers should be encouraged to use their library’s resources in the context of improving their scholarly contribution.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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".