WAAI: A Weighted Author Affiliation Index for Journal Evaluation
Bibliographic record
Abstract
Journal evaluation methodologies are often used to produce journal-ranking lists for various purposes. In this study, the authors present a modularity-based journal evaluation methodology based on the proportional contributions that journals receive from authors affiliated with globally ranked institutions. This empirically developed methodology draws on a stratification of institutions in the global rankings to allocate weights to article batches in a given journal. The authors apply the proposed methodology to evaluating 12,150 scholarly journals in different subject fields. The results show an elitist set of journals with a heavy tendency to publish mostly from authors affiliated with the top-ranked institutions. These journals have the highest weighted author affiliation index (WAAI) scores and are highly distinguished titles in different subject fields. However, the authors find a large population of journals that receive contributions mostly from institutions at lower ranks. They argue that the WAAI methodology provides a generic and objective evaluation technique for ranking journals across all disciplines.
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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 | 0.019 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.031 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".