Bibliographic record
Abstract
Publishing your research requires knowing about the business practices of journals, what journal editors and peer reviewers want, and how the publication process works. For example, journals that have to make money for their owners have different needs and requirements than journals funded by government agencies or universities, and journals that receive advertising have different needs and requirements than journals that receive article processing charges from authors. Some journals are directed to readers in several public health disciplines, whereas others are directed to specialists or subspecialists. Finally, some journals are directed to international audiences, whereas others are directed to national or regional audiences. The quality or impact of a journal also has to be assessed before submitting a manuscript and when evaluating articles and authors who have published in it. Each of these characteristics should be considered when choosing a target journal. Likewise, most journals follow strict ethical standards when accepting, reviewing, and publishing articles, but other “predatory” journals do not, which can cost unsuspecting authors money and never result in a legitimate publication. Many authors, especially those early in their careers, are unfamiliar with the strengths and weaknesses of the various forms of peer review and how to respond to reviewers’ comments. Here, I review the scientific publishing process, including what authors need to know about journals, manuscript preparation and submittal, publication ethics, peer review, and other journal requirements.
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 | 0.152 | 0.518 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.054 | 0.035 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.027 | 0.045 |
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".