Out-of-home advertising media: theoretical and industry perspectives
Bibliographic record
Abstract
Out-of-home (OOH) advertising media traditionally have not accounted for a large share of advertising budgets, but overall expenditure has grown considerably in recent years. Due to the transformation of the OOH advertising media landscape, and the diversity and ubiquitous nature of these media, there seem to be a discrepancy between the views of academic and industry experts on exactly what constitutes contemporary OOH advertising media. This article addresses the identified academic-practitioner divide by presenting both sides of the coin. An integrative review of OOH advertising media taxonomies in prominent academic sources, as well as specialists’ industry publications from Canada, South Africa, America, Australia, Ireland and the United Kingdom, was conducted. This resulted in a new conceptualisation of four key platforms for a contemporary OOH advertising media classification framework: outdoor advertising, transit media advertising, street-and-retail-furniture advertising, and digital and ambient OOH media. Clear direction for future research was given, specifically testing the proposed conceptualisation, the impact of OOH audience environments and mood on message delivery, and digital OOH advertising as one of the fastest growing media types.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 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".