Comparing the influence of ecology journals using citation-based indices: making sense of a multitude of metrics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The links among scholarly citations creates a tremendous network that reveals patterns of influence and flows of ideas. The systematic evaluation of these networks can be used to create aggregate measures of journal influence. To understand the citation patterns and compare influence among ecology journals, I compiled 11 popular metrics for 110 ecology journals: Journal Impact Factor (JIF), 5-year Journal Impact Factor (JIF5), Eigenfactor, Article Influence (AI), Source-Normalized Impact per Paper (SNIP), SCImago Journal Report (SJR), h-index, hc-index, e-index, g-index, and AR-index. All metrics were positively correlated among ecology journals; however, there was still considerable variation among metrics. Annual Review of Ecology, Evolution, and Systematics, Trends in Ecology and Evolution, and Ecology Letters were the top three journals across metrics on a per article basis. Proceedings of the Royal Society B, Ecology, and Molecular Ecology had the greatest overall influence on science, as indicated by the Eigenfactor. There was much greater variability among the other metrics because they focus on the mostly highly cited papers from each journal. Each influence metric has its own strengths and weaknesses, and therefore its own uses. Researchers interested in the average influence of articles in a journal would be best served by referring to AI scores. Despite the usefulness of citation-based metrics, they should not be overly emphasized by publishers and they should be avoided by granting agencies and in personnel decisions. Finally, citation-based metrics only capture one aspect of scientific influence, they do not consider the influence on legislation, land-use practices, public perception, or other effects outside of the publishing network.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it