Expanding <scp><i>Nature</i></scp>: <scp>Product line</scp> and brand extensions of a scientific journal
Bibliographic record
Abstract
Abstract Academic publishers now market their most prestigious journals as commercial brands. This paper investigates this trend in the scholarly publishing market, by analyzing how the successive owners of the journal Nature have capitalized on its reputation to generate additional profits to those already accumulated through university library subscriptions. Two branding strategies of the journal Nature are analyzed: the first one, product line extension, consists in extending the Nature brand in the same product category, by creating an ever‐increasing number of derived Nature journals; the second one, brand extension, consists in extending the Nature brand to other categories of products and services, such as academic rankings, sponsored supplements, feature advertisements, or webinars and trainings. The Nature brand leveraging strategy has been imitated by many other journal publishers. These branded products and services are well suited to the particular dynamics of the scientific field, which is based on the continuous quest for recognition. They are thus sold at all stages of the research cycle, from writing grants to popularizing research results, to scientists and academic institutions competing to accumulate symbolic capital. In this respect, academic publishers that engage in scholarly journal branding contribute to the transformation of the scientific ‘community’ into a scientific market.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".