Bibliographic record
Abstract
Referencing refers to any form of citing a source in a document. References can be detailed or non-detailed. The sources to which the references refer can be diverse, and regardless of the types, they can be in analogue or digital form. Anonymous sources are undesirable and should be avoided as much as possible. This means putting more effort into searching for alternative sources and finding relevant ones, with a preference for the primary ones. If the author does not state the source from which he took some information, then it is assumed that it is the author's original work, and if it is not already taken from an unlisted source, then we are talking about plagiarism and its possible consequences and sanctions. Referencing is necessary not only when using other author's texts, but also when using their tables, graphs, figures, diagrams, etc., anything that is not the result of the author's work. Many styles are used for reference, but most of them can be grouped into three main types: text referencing, footnote referencing, and numerical referencing. The most famous styles are Harvard, American Psychological Association (APA), Modern Language Association (MLA), Vancouver, Institute of Electrical and Electronics Engineers (IEEE), Oxford, and Chicago. Regardless of what type and style of referencing are used, it is important to be clear about what is taken from external sources and what is the author’s original contribution.Referencing is often a tedious process, requiring a lot of time and the necessary consistency of using the same style within a single document, so the software can help us greatly in this task. This article presents some commonly used reference management programs. Three studies were conducted, the results of which are presented in the article. The first is a study on the use of reference styles of Croatian scientific and professional journals available through Hrčak - Portal of scientific journals of Croatia, using their instructions for authors. The second research is the analysis of instructions for students of faculties and departments of Croatian universities. The third survey was conducted through a questionnaire completed by one hundred students, which provided insight into their knowledge and practice of using reference styles and accompanying software.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".