The accuracy of references in <scp>P</scp>h<scp>D</scp> theses: a case study
Bibliographic record
Abstract
BACKGROUND: Inaccurate references and citations cause confusion, distrust in the accuracy of a report, waste of time and unnecessary financial charges for libraries, information centres and researchers. OBJECTIVES: The aim of the study was to establish the accuracy of article references in PhD theses from the Tehran and Tabriz Universities of Medical Sciences and their compliance with the Vancouver style. METHODS: We analysed 357 article references in the Tehran and 347 in the Tabriz. Six bibliographic elements were assessed: authors' names, article title, journal title, publication year, volume and page range. Referencing errors were divided into major and minor. RESULTS: Sixty two percent of references in the Tehran and 53% of those in the Tabriz were erroneous. In total, 164 references in the Tehran and 136 in the Tabriz were complete without error. Of 357 reference articles in the Tehran, 34 (9.8%) were in complete accordance with the Vancouver style, compared with none in the Tabriz. Accuracy of referencing did not differ significantly between the two groups, but compliance with the Vancouver style was significantly better in the Tehran. CONCLUSIONS: The accuracy of referencing was not satisfactory in both groups, and students need to gain adequate instruction in appropriate referencing methods.
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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 | 0.017 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".