Global Brand Identity as a Network of Localized Meanings
Bibliographic record
Abstract
In this article, we develop a semiotic model to analyze advertisement glocalization. This model focuses on the mental representations that local audiences build of a “global” brand identity. We demonstrate how this model fills up gaps left by a popular marketing model for global advertising. We argue that the seemingly linear three- step marketing model implies several reciprocal processes in which meaning is developed and determined. This semiotic reinterpretation of the marketing model explains how a global brand identity maintains a dynamic relation with the actual brand identity that local customers construe. To illustrate the dynamics of the semiotic model, we analyzed localizations in the Snickers campaign “You’re not you when you’re hungry.” Because the semiotic model elaborates the dynamics between the professionals’ discourse used in developing a campaign and the localized “global” brand identities brought about in receiving the campaign, the model helps to explain anthropological dynamics in designing campaigns, the arising of locally differentiated “global” brand identities that are the result of global campaigns and the dynamic development of global campaigns.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".