Bibliographic record
Abstract
Communicating the results of research using concise, jargonfree language optimize its likelihood of being read and cited by other researchers. More than 3 decades of publishing scientific articles has convinced us that the most efficient approach to writing a scientific paper is one that starts in the middle and works outward. Such an approach means that the first items to finalize and polish are the actual tables and figures that will be included in the body of the paper (including supplemental tables and figures). This entails deciding on the order of these elements that, when viewed alone, should be able to tell the story of the paper. This first step is often the most difficult, requiring the most thought. However, once achieved, it is usually a straightforward process to write the Results section (that refer to these elements) followed by the Methods section. After these sections are proofed and polished, a quick review of the Methods, Results and associated figures and tables highlight the points that need to be made in the Discussion section. The last sections written should be the Introduction, to set up the entire Methods, Results and Discussion, and the Abstract, to summarize it all. When this approach is combined with frequent proofreading of the article on some medium that is different from the one used to write it, experience has shown that the result will be a clear, uncluttered paper that is completed with a minimal number of drafts and that is most likely to be favorably reviewed and accepted for publication.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.000 |
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 teacher head, 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".