On translating the ‘bible of marketing’
Bibliographic record
Abstract
In 1967, Prentice Hall publishers released the first edition of Marketing Management by Philip Kotler. Since then and through more than four decades, this title has established itself as a bestseller with an exceptional history of transformations. As a canonical graduate-level textbook in a popular discipline in which textbooks are myriad, Marketing Management can be taken as an archetype for the study of translation practices in higher education publishing – an influential sector of the book industry which heavily relies on translation (both in its restricted and extended definitions) but has thus far received scant attention in translation studies. The present article focuses on interlingual translations of Kotler’s volume in three languages (Spanish, French and Italian), which are considered in relation to other forms of rewritings such as new editions and English-language adaptations. The authors analyse and compare the formal characteristics of these interlingual translations; they explore by whom and for what purposes they were initially produced, as well as how they evolved over time. While highlighting the specific trajectory of Marketing Management in each language, the study also reveals common features and questions to what extent these reflect the agency of those who produced the translations as well as ongoing transformations in higher education publishing.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".