Marketing Journal Rankings: Active Scholar Assessment
Bibliographic record
Abstract
Abstract Journal rankings convey important information to researchers and influence processes related to promotion, remuneration, research funding, and resource allocation in academe. The present research uses direct responses from an international sample of 203 active marketing scholars in a web-based survey to endogenously rank 138 marketing journals by quality, awareness, and importance. We employ regression estimation with nested random journal-within-tier effects to comprehensively rank the marketing journals into four ordered tiers (A–D), and then in turn, subdivide journals in each tier, into “upper,” “middle,” and “lower” groups (e.g. Tier A: A+, A and A−). Our methodology, Active Scholar Assessment (ASA), produces an independent ranking of marketing journals that aggregates individual expert opinion regarding journals by researchers from 68 countries. Subsequently, we compare our ASA-developed marketing journal rankings and categories with prominent citation-based ranking systems (Scimago, Clarivate Analytics’ Journal Citation Reports, Association of Business Schools, and the Australian Business Deans Council) to demonstrate that the opinions of active scholars are comparatively more stable and capture additional information (that is not reflected by computations based solely on citations), and provide useful strategic information and direction to scholars.
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 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 | 0.093 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".