Bibliographic record
Abstract
Publication of research findings and novel concepts in the biomedical literature is the mainstay of knowledge mobilization. The communication of scientific work as papers follows a well‐established framework. A clear understanding of the nature of this framework and how to assess one's own work against it is critical to successful acceptance and subsequent publication of manuscripts. This paper reviews the standard framework for scientific communication. Communicating your findings is a form of scientific storytelling. Generally, an author must capture the interest of a potential reader right at the abstract, which is sometimes the only way a reader sees the paper if they have discovered the work using a search engine. In the main body of the paper, the author must clearly explain how the study was designed and carried out with careful attention paid to any important details and to the statistical analysis, if appropriate. Results must be presented in a manner that is clear and easily interpreted by the reader. Then, the work should be discussed in the context of other work in the area, emphasizing the novel findings. This review will also address the issues of deciding where to publish and what happens to the paper after submission. Our intent is to provide general guidance for the publication of papers in the biomedical literature rather than be focused on publication in any specific journal.
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 | 0.072 | 0.430 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.044 | 0.026 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.163 | 0.252 |
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".