A critical evaluation of expert survey‐based journal rankings: The role of personal research interests
Bibliographic record
Abstract
By using the data from two recent survey‐based rankings of knowledge management / intellectual capital and eHealth journals, this study tests the impact of personal research interests of journal raters on their ranking scores. The rationale is that raters assign higher scores to journals that cater to their area of expertise because they are more familiar with them. The results indicate the existence of raters’ bias toward the journals focusing on their preferred areas of interest, but this bias does not uniformly apply across all research topics. In some subdomains, such as intellectual capital, this bias may be very strong, whereas in others, such as soft‐side knowledge management research, it may be nonexistent. Although management eHealth researchers rate management‐focused journals higher than their clinical‐centered counterparts, this bias does not exist among scholars favoring clinical topics. While this limitation is not fatal, the results from expert‐survey journal ranking studies should be interpreted with caution.
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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 | 0.541 | 0.826 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier 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".