Bibliographic record
Abstract
Bibliometrics is an important aspect of determining the research impact of a scientific article. Although the research impact of an article can be measured in several ways, citation counts are a popular and straightforward metric to determine the number of times a research article is cited in another article or book. In the science, technology, engineering, and math (STEM) fields, Scopus and Google Scholar are two tools that can be used to determine citation counts. However, the extent to which Google Scholar and Scopus index policy citation counts of STEM articles is unknown. This research compares the citation counts of 25 environmental science scholarly articles from five different authors across Google Scholar, Scopus, and a policy database called Overton. By identifying the citation count differences between the three tools to identify gaps in citation count metrics, this study concludes that Overton overwhelmingly identifies policy citation counts that are not found by Google Scholar or Scopus and thus is an important database to consider when analyzing citation counts in the environmental science field. This finding is significant because determining scientific articles’ impact in the policy field demonstrates how scientific literature can make real world impacts.
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 | 0.046 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.042 | 0.071 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".