“Who Cares?” Defining Citation Style in Scholarly Journals by Vincas Grigas and Pavla Vizváry
Bibliographic record
Abstract
This poster is presented by Vincas Grigas Vilnius University. Summary of abstract: Dr. Ian Malcolm from Jurassic Park once said, “Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.” This applies to academic publishing because it has numerous citation styles (10,377 in fact) that cause confusion. Crossref, which registers digital identifiers, reported that only half its registered articles have reference lists. A study of 270 reputable journals showed that most don’t require a specific citation style, giving authors examples instead. APA was the next common style, with others like Vancouver, Harvard, Chicago following. Even unnamed citation styles often matched known styles like APA and Harvard, but with minor changes. Despite digital identifiers’ importance, only 41.1% of journals requested them from authors, yet 78.1% did include them. The citation style choices were influenced more by the journal’s scientific field and less by the country of origin.
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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".