Bibliographic record
Abstract
Scientific writing is the communication of a new idea that will alter or enlarge the reader's understanding of the subject of the article. Scientific writing follows a standard template defined by the journal that published the article. Authors greatly increase the chance that their manuscript will be published by reading and following the journal's instructions to authors. Every word, image, and diagram in an article should serve the purpose of communicating the author's new idea effectively and forcefully. The title, introduction, method, results, discussion, and reference list serve different purposes within the article and therefore are constructed differently. The abstract is a distillation of the essential points of the article and should always contain the central idea that the article was written to convey. Anyone listed as an author must have made a meaningful contribution to the work; that is, without each author's contribution, the article simply would not exist. Authors must avoid plagiarism by neither copying the writings of others nor copying their own writings or images used in previous publications. Plagiarism is easily avoided by taking notes, not quotes, from articles that serve as references and then writing the new manuscript from these notes; this system changes the original wording of the references twice and transforms the concepts into the author's voice.
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 | 0.234 | 0.405 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.015 | 0.087 |
| Scholarly communication | 0.050 | 0.018 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.021 | 0.049 |
| Insufficient payload (model declined to judge) | 0.016 | 0.039 |
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".