Bibliographic record
Abstract
Abstract In the introductory chapter, the editors for The Oxford Handbook of Music and Advertising provide the foundation for the three main sections of the volume, in accordance with three stages of communicating the advertising message established by Wharton (2015): “Production,” “Text,” and “Reception.” The discussion of “Production” considers the contexts for the creation of audiovisual advertising, first as studied in the scholarly literature, and then according to practices and producers. Also under examination is the cultural work music performs for advertising, with special emphasis on branding. The section on “Text” focuses on the various forms and functions of music in advertising media. Text analysis in multimedia formats include discussions of how music combines with visual images and speech to convey an emotion, meaning, or ethos to an ad. Also under discussion in this part of the chapter is how music functions in commercials, both as foregrounded text and as background. Finally, the section addressing “Reception” discusses specific challenges facing researchers undertaking empirical studies on music and advertising, explores the psychological underpinnings of theory and research in this area, and points to the opportunity for more cross-fertilization of ideas between fields and greater interdisciplinary collaboration.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.518 | 0.333 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".