Do citations and impact factors relate to the real numbers in publications? A case study of citation rates, impact, and effect sizes in ecology and evolutionary biology
Bibliographic record
Abstract
Metrics of success or impact in academia may do more harm than good. To explore the value of citations, the reported efficacy of treatments in ecology and evolution from close to 1,500 publications was examined. If citation behavior is rationale, i.e. studies that successfully applied a treatment and detected greater biological effects are cited more frequently, then we predict that larger effect sizes increases study relative citation rates. This prediction was not supported. Citations are likely thus a poor proxy for the quantitative merit of a given treatment in ecology and evolutionary biology-unlike evidence-based medicine wherein the success of a drug or treatment on human health is one of the critical attributes. Impact factor of the journal is a broader metric, as one would expect, but it also unrelated to the mean effect sizes for the respective populations of publications. The interpretation by the authors of the treatment effects within each study differed depending on whether the hypothesis was supported or rejected. Significantly larger effect sizes were associated with rejection of a hypothesis. This suggests that only the most rigorous studies reporting negative results are published or that authors set a higher burden of proof in rejecting a hypothesis. The former is likely true to a major extent since only 29 % of the studies rejected the hypotheses tested. These findings indicate that the use of citations to identify important papers in this specific discipline-at least in terms of designing a new experiment or contrasting treatments-is of limited value.
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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 | 0.050 | 0.418 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.040 | 0.107 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".