Lost in Translation: The Social Shaping of Marketing Messaging
Bibliographic record
Abstract
Abstract Word of mouth marketing (WOMM) does not travel as unidirectionally and straight as previously assumed. Rather, consumers are active co-producers of value and translate and transform marketing meanings. Word of mouth resulting from marketing communications can be anything from euphoric to resistant, and the discourse evolving around a product seeding has a strong impact on how this product is perceived. As messages become translated into meaningful, communally shared material, particular cultural restraints cause possibilities open up, rules to become less constraining, and the principles and guidelines for successful social branding engagement to become much more about human relationships than one-way communication. Social brand engagement is a genuine, natural interconnection between brand mentions and consumer-to-consumer social experiences. Therefore, simply observing the reach and valence of product mentions is too short sighted. To effectively attain social brand engagement, promotions need to seem authentic and congruent with people, media, other content and the offline or online context. A deep analysis of what is going on in the prospective environment of a message to be seeded is a precondition for the optimal design of a WOMM campaign and the accurate interpretation of its success.
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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".