Marketing Love: Romance Publishers Mills & Boon and Harlequin Enterprises, 1930–1990
Bibliographic record
Abstract
When Harlequin Enterprises acquired British publisher Mills & Boon in 1972, the merged firm became the world’s dominant publisher of popular romance novels. Little is known, however, about the role that innovative marketing strategies played in the growth of these two romance publishing companies, especially their use of product sampling, direct mail, product standardization, and what was known at Mills & Boon as the “personal touch.” Through research in the Mills & Boon company archive at the University of Reading, the Grescoe Archive at the University of Calgary, as well as an analysis of company histories, trade publications, interviews, and marketing techniques, this study reveals how Harlequin and Mills & Boon took a different approach to product promotion than traditional publishers. Their innovation was to incorporate consumer goods marketing strategies, familiar to other industries, that disrupted and redefined standard practices of book publishers.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".