Global Brands in Anti-Globalization Backlashes, Whitelashes, Greenlashes, and Wokelashes: Perspectives and Challenges
Bibliographic record
Abstract
Abstract This chapter focuses on hostility toward globalization and angst toward the attendant movements of diversity, environmentalism, and “wokeism.” The authors examine where this antagonism comes from, how it manifests, and, ultimately, what the potential consequences are for global brands. The causes and effects of the current period of disruption are many. Angry and anxious people have a need to focus their negative affect on a target, and globalization represents a handy scapegoat. In the first part of this chapter, the main types of consumer reactions to the current environment are outlined. Anti-globalization sentiments are not uniform, as they arise from a variety of economic, technological, social, and ideological forces. The second part examines the underlying emotional and cognitive factors that reflect some of the root causes of the consumers’ discontent. Different motivations are distinguished, and accordingly, different responses to each must emerge. The third part highlights implications for global brand marketing, of which the most conceivable and serious outcome is brand avoidance. The authors provide managers with the key elements of the marketing toolbox that might enable firms and brands to navigate the current rough seas.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".