Referencing Style: A study of PhD Theses in the University of Calcutta during 2019 to 2023 under the Faculty Council for Post-Graduate Studies in Engineering & Technology
Bibliographic record
Abstract
Abstract Purpose: This study aimed to investigate the citation styles followed by researchers under the Faculty Council for Post-Graduate Studies in Engineering and Technology at the University of Calcutta between 2019 and 2023. It sought to identify the predominant referencing patterns and assess the extent of consistency in their application. Methodology: A quantitative content analysis was conducted using dissertations available in the Shodhganga digital archive. Each dissertation was manually reviewed, categorized by department and year, and examined to identify the citation styles used. The analysis focused on commonly adopted styles such as IEEE, APA, Vancouver, Harvard, and the prevalence of mixed or inconsistent formats. Findings: The results revealed that while IEEE, APA, Vancouver, and Harvard were the most frequently used styles, a significant number of dissertations relied on mixed citation formats. These were often applied inconsistently, reflecting the absence of a standardized referencing policy across departments. The study also observed that some disciplines demonstrated limited awareness of the variety of citation styles available or their importance in ensuring academic rigor. Implications: The findings highlight the need for a university-wide referencing guideline to ensure uniformity in citation practices. Additionally, targeted training for researchers and stronger supervisory oversight are recommended to enhance academic quality, maintain consistency, and uphold scholarly integrity in doctoral research.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.023 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".