Branding Spin-Off Scholarly Journals: Transmuting Symbolic Capital into Economic Capital
Bibliographic record
Abstract
In this article, we analyse a relatively recent commercial strategy used by large academic publishers to capitalize on the brand names of their most prestigious scientific journals. Using Pierre Bourdieu’s model of capital conversion, we explain how publishers transfer the symbolic capital of an already prestigious journal to derivative journals that share in the prestige of the original brand and transform it into new economic capital. As shown by their high impact factors, these newly created journals benefit from the name recognition and reputation of the originals after which they are named. Plus, through a manuscript routing mechanism, the publishers recycle some of the submissions rejected by their highly selective flagship journal by redirecting those manuscripts, along with their reviews, to derivative journals or to one of the lower-impact journals on their list, which may require an article processing charge for publication.
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 | 0.081 | 0.214 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.046 | 0.055 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.385 | 0.268 |
| Open science | 0.009 | 0.001 |
| Research integrity | 0.000 | 0.007 |
| 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".