Dancing with the devil: the use and perceptions of academic journal ranking lists in the management field
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This study explores the use and perceptions of scholarly journal ranking lists in the management field based on stakeholders’ lived experience. Design/methodology/approach The results are based on a survey of 463 active knowledge management and intellectual capital researchers. Findings Journal ranking lists have become an integral part of contemporary management academia: 33% and 37% of institutions and individual scholars employ journal ranking lists, respectively. The Australian Business Deans Council (ABDC) Journal Quality List and the UK Academic Journal Guide (AJG) by the Chartered Association of Business Schools (CABS) are the most frequently used national lists, and their influence has spread far beyond the national borders. Some institutions and individuals create their own journal rankings. Practical implications Management researchers employ journal ranking lists under two conditions: mandatory and voluntary. The forced mode of use is necessary to comply with institutional pressure that restrains the choice of target outlets. At the same time, researchers willingly consult ranking lists to advance their personal career, maximize their research exposure, learn about the relative standing of unfamiliar journals, and direct their students. Scholars, academic administrators, and policymakers should realize that journal ranking lists may serve as a useful tool when used appropriately, in particular when individuals themselves decide how and for what purpose to employ them to inform their research practices. Originality/value The findings reveal a journal ranking lists paradox: management researchers are aware of the limitations of ranking lists and their deleterious impact on scientific progress; however, they generally find journal ranking lists to be useful and employ them.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it