Dancing with the devil: the use and perceptions of academic journal ranking lists in the management field
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.
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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 | 0.024 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".