A model of periodization of radio and internet advertising history
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it