Bibliographic record
Abstract
At one time, there were over 250 different styles of reference in the scientific literature (Garfield 1986). The editors of some major biomedical journals therefore had good reason to convene in Vancouver, Canada, in January 1978, to work out a uniform reference style. One of their suggestions was that authors should number references in the order in which they appear in the text (International Committee of Medical Journal Editors 1997). Vancouver versus Harvard style? Although many of the major journals in the biomedical field have adopted the Vancouver style, some still prefer the Harvard system (first used in 1881 by a zoologist at Harvard University [Chernin 1988]) in which the author's name and the year of publication are cited in the text. In the fictive sentence below, I have mixed the two styles to illustrate their differences: A reference figure (17) in the Vancouver style says less than a name-and-year reference (Einstein 1941) according to the Harvard system. Most readers prefer the Harvard system because they like to know just what author is being cited as they read the text. Still, the name-and-year system does have disadvantages: difficulty for readers who see an interesting item in the reference list in locating that reference in the main text; and, more important, the disruption of the text when a large number of references need to be cited within a paragraph, as in this example (Bengtsson 1968): This method was introduced by Aburel in 1938, but he was followed by only a few workers in the succeeding 20 years (Bommelaer 1948; Cioc 1948; Kosowski 1949; de Watteville and d'Enst 1950).[…]
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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.306 | 0.241 |
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".