Who should rank our journals … and based on what?
Bibliographic record
Abstract
Purpose This study aims to establish the use of active scholar assessment (ASA) in the field of education leadership as a new methodology in ranking administration and leadership journals. The secondary purpose of this study is to respond to the paucity of research on journal ranking in educational administration and leadership. Design/methodology/approach This empirical study uses on‐line survey research methods with analysis of variance (ANOVA) statistical analysis. Findings The main findings of this study are: ASA minimizes noted limitations in peer assessment studies; publishing rates and years of service do not significantly influence quality assessment bias; ASA provides a comprehensive and fair assessment of journals; and ASA responds to established criteria as a new, independent system for journal ranking. This study also provides current rankings of educational administration and leadership journals. Research limitations/implications This study points to the importance of continued research using ASA in journal assessment in education and other social sciences. Originality/value This study provides a new methodology in assessing journal quality, awareness, and importance to the field for journals in educational administration and leadership.
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 | 0.027 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".