Bibliographic record
Abstract
Blue Ocean Strategy was first formulated in 2005 by Chan and Maubourgne as a methodology to create uncontested marketspace and make the competition irrelevant for products and services in a business setting.The concept of Blue Ocean Strategy, however, can be applied to a broader range of industries and applications beyond the world of business.The present paper will apply the concept of Blue Ocean Strategy and its tools to demonstrate a new use for the Blue Ocean Methodology, through a case analysis involving the success of the Twilight series of books and movies by Stephanie Meyer, and its comparison to other Vampire books, including Bram Stoker's Dracula and the TV series The Vampire Diaries and The Originals.The paper will argue that the same concepts and tools found in comparing blue ocean and red ocean in businesses as applied in this case, can be utilized, in the arena of the literary world for book authors and screenwriters seeking to develop new characters and story plotlines, or for Hollywood/Film use in developing new television shows, sitcoms, or movies.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".