Variable Bibliographic Database Access Could Limit Reproducibility
Bibliographic record
Abstract
Bibliographic databases provide access to scientific literature through targeted queries. The most common uses of these services, aside from accessing scientific literature for personal use, are to find relevant citations for formal surveys of scientific literature, such as systematic reviews or meta-analysis, or to estimate the number of publications on a certain topic as a measure of sampling effort. Bibliographic search tools vary in the level of access to the scientific literature they allow. For instance, Google Scholar is a bibliographic search engine which allows users to find (but not necessarily access) scientific literature for no charge, whereas other services, such as Web of Science, are subscription based, allowing access to full texts of academic works at costs that can exceed $100,000 annually for large universities (Goodman 2005). One of the most commonly used bibliographic databases, Clarivate Analytics–produced Web of Science, offers tailored subscriptions to their citation indexing service. This flexibility allows subscriptions and resulting access to be tailored to the needs of researchers at the institution (Goodwin 2014). However, there are issues created by this differential access, which we discuss further below.
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 | 0.450 | 0.836 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.020 | 0.044 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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".