Building Credibility with Comprehensive Citation Practices
Bibliographic record
Abstract
Honoring those who paved the way and paving the way for others is an old saying and practice that emphasizes the importance of acknowledging the contributions of others while also guiding and mentoring those who follow in your footsteps. In academic and scientific writings, citations are very important for maintaining the integrity, credibility, and progression of scientific knowledge. This article examines the significance of citations, their various types and methods, and the different styles used. By focusing on best practices and common errors, this article aims to guide researchers in effectively incorporating citations to enhance their work's visibility and impact. It analyses the different types of citations, including direct quotes, paraphrases, and summaries, and discusses the major citation systems such as the Vancouver and Harvard styles. The article also examines the common errors in citation practices and offers guidelines for accurate referencing. It also reviews various reference management software that facilitate to organize and automate the citation process. The impact of citations on bibliometric measures such as impact factor, H index, CiteScore, etc., which assess the influence and productivity of research, is also discussed. Further, the article briefly delves into the utility of artificial intelligence in citation management and future directions in citation practices. The goal of this article is to elevate the quality and credibility of academic publications in the area of medicine by focusing on the principles and methods of effective citation.
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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 | 0.329 | 0.733 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.052 | 0.040 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.029 | 0.036 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".