Bootstrapping to evaluate accuracy of citation-based journal indicators
Bibliographic record
Abstract
Introduction Bibliometric indicators ranking aggregate units have a long tradition, including criticisms of methodology, interpretation and application. Despite the criticism, there is a demand for these indicators, and recent developments have led to improvements of methodology and interpretation. An essential element of these interpretations is to provide estimates of the accuracy, robustness, stability and confidence of bibliometric indicators, thereby providing the reader with data required to interpret results. This has, for example, been demonstrated for the set of indicators in the Leiden ranking (Waltman et al., 2012), the Journal Impact Factor (Chen, Jen, & Wu, 2014) and other journal indicators (Andersen, Christensen, & Schneider, 2012) as well as author metrics (Lehmann, Jackson, & Lautrup, 2008). The present study applies the same type of bootstrapping technique to estimate stability, as is used in the Leiden ranking (Waltman et al., 2012), on an array of citation-based journal indicators. The purpose of this analysis is to compare recent methodological advances, as well as traditional approaches. The study is based on clinical medicine journals in the Web of Science (WoS).
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How this classification was reachedexpand
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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.067 | 0.124 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".