A model of periodization of radio and internet advertising history
Bibliographic record
Abstract
Purpose This paper aims to describe the development of forms of advertising on radio and internet when they were new media and propose a model of periodization through which the two histories can be understood and appreciated. Design/methodology/approach Two narrative histories were constructed based on data collected from numerous public and private, historical and contemporary and primary and secondary materials. The methodology of New Historicism informed the research. Findings When the two histories are viewed through the model, many similarities in terms of milestones and markers become apparent. Research limitations/implications Perhaps when the next new electronic mass medium is invented, a future researcher may look back on this model and consider whether it applies. Practical implications For practitioners who consider history a relevant source of knowledge and inspiration, this research offers a way of organizing and understanding the history of internet advertising. Social implications Today’s consumers, especially Millennials, continue to seek to avoid advertising on the internet. The use of ad blockers poses a significant threat to the business models of online content providers. This research demonstrates that resistance to advertising is nothing new and that it may be, in the end, futile. Originality/value The model is an original creation, based on an original view of history, and offered as a lens through which to understand this history.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".