The Evolution of Viral Marketing to Improve Business Communication
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
To win the consumers attention, more prone to advertisement, it is essential that companies interact with them. Equally important for the effectiveness of an advertising campaign is the ability to involve, amaze and entertain users in such a way as to encourage them to talk about a brand or product, spontaneously triggering a viral word of mouth. To achieve this, companies use different communication tools, especially web communication and digital marketing. Companies can choose to approach to these new phenomena, read them, understand them, interpret them, research and identify new advantages and opportunities; then start a process of change aimed at adapting the organization to a model that is able to fully exploit these phenomena. Or they could choose to ignore them, distance them, close their eyes, pretend they do not exist, convince themselves that they are only transitory phenomena of a technological nature and lacking relevance for the business. The goal of the work is to verify how the viral marketing instrument can help improve and strengthen business communication. In fact, by now, there are many companies that have decided to support and, in some cases, replace traditional communication with online communication.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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