A Design Methodology for a Biomedical Literature Indexing Tool Using the Rhetoric of Science
Bibliographic record
Abstract
Literature indexing tools provide re-searchers with a means to navigate through the network of scholarly scientific articles in a subject domain. We propose that more effective indexing tools may be designed using the links between articles provided by citations. With the explosion in the amount of sci-entific literature and with the advent of ar-tifacts requiring more sophisticated index-ing, a means to provide more information about the citation relation in order to give more intelligent control to the navigation process is warranted. In order to navigate a citation index in this more sophisticated manner, the citation index must provide not only the citation-link information, but also must indicate the function of the cita-tion. The design methodology of an in-dexing tool for scholarly biomedical lit-erature which uses the rhetorical context surrounding the citation to provide the ci-tation function is presented. In particular, we discuss how the scientific method is re-flected in scientific writing and how this knowledge can be used to decide the pur-pose of a citation. 1
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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".