Bibliographic record
Abstract
Advertising, long a controlling force in industrial society, has provoked an important body of imaginative work by English language writers. Michael Ross's Designing Fictions is the first study to investigate this symbiotic relationship on a broad scale. In view of the appreciable overlap between literary and promotional writing, Ross asks whether imaginative fiction has the latitude to critique advertising as an industry and as a literary form, and finds that intended critiques, time and again, turn out to be shot through with ambivalence. The texts considered include a wide range of books by British, American, and Canadian authors, from H.G. Wells’s pioneering fictional treatment of mass marketing in Tono-Bungay (1909) to Joshua Ferris’s depiction of a faltering Chicago agency in Then We Came to the End (2007). Along the way, among other examples, Ross discusses George Orwell’s seriocomic study of the stand-off between poetry and advertising in his 1936 novel Keep the Aspidistra Flying and Margaret Atwood’s probing of the impact of promotion on perception in The Edible Woman (1969). The final chapter of the book considers the popular television series Mad Men, where the tension between artistic and commercial pressures is especially acute. Written in a straightforward style for a wide audience of readers, Designing Fictions argues that the impact of advertising is universal and discussions of its significance should not be restricted to a narrow group of specialists.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.014 |
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".