Bibliographic record
Abstract
Abstract If “consumer brand engagement” is what happens in isolation, in a consumer’s own individual mind and thoughts, then “social brand engagement” is the diametrical opposite of this. Social brand engagement is a social act full of culture, meaning, language, and values. With social brand engagement, relationships widen from person-brand to person-person-brand. This can take different forms. While some consumers remain passive, others act more or less creatively in favor of or against brands. Some marketers are happy with the forms of evangelizing in which consumers simply spread brand messages. But the most authentic and believable form of endorsement, and therefore the optimal state, is marked by the creative expression and use of the brand. Here, people play positively and socially with the brand. They view it as a valued and valuable cultural resource and such social brand engagement has meaningful social, creative and productive outcomes. In successful social brand engagement, both consumers and producers play active roles, but one party has to take the lead. Companies have historically had major problems letting consumers take over some of their former responsibilities. For successful authentication to happen, however, putting consumers in the driver’s seat is sometimes—but certainly not always—necessary.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.040 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".