Citation Metrics for Editors of Top-Ranked Journals Related to Higher Education: A Descriptive Study
Bibliographic record
Abstract
Today’s university faculty members engage in myriad activities related to the three general work categories of teaching, research, and service. In order to satisfy the evaluative process for tenure and promotion as related to the research category, the faculty member typically must present not only a curriculum vitae that establishes a substantive record of scholarly productivity but also indicants of impact to the field. Though these latter indicants have often been via letters of support from the faculty member’s discipline, increasingly the provision of citation metrics are being used. But while such metrics are meant to provide support for advancement, their use is rather ambiguous due to the lack of defined standards of performance; that is, without standards, how can provided metrics be interpreted? Because chief editors of prestigious journals are typically seasoned scholars, the purpose of this descriptive study is to characterize the citation metrics—citation count, h-index, and i10- index—for the chief editors of 10 top-ranked journals related to the field of higher education. This field was chosen because such editors are likely fully engrossed in the study, practice, and traditions of higher education and, thus, should represent a professorial standard for comparison and, perhaps, a goal for a university faculty member’s scholarly productivity. For this descriptive study, citations metrics were available for 11 out of 16 chief editors whose institutions represented the countries of Australia, Canada, Colombia, Spain, United Kingdom, and United States of America. Findings suggest these 11 chief editors are widely cited academicians and, thus, provide salient standards for the purpose of this discussion.
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.
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 | 0.007 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".