Understanding Global Cultures: Metaphorical Journeys Through 29 Nations, Clusters of Nations, Continents, and Diversity
Bibliographic record
Abstract
From the Publisher: In Understanding Global Cultures, Fourth Edition, authors Martin J. Gannon and Rajnandini Pillai present the cultural metaphor as a method for understanding the cultural mindsets of individual nations, clusters of nations, and even continents. The fully updated Fourth Edition continues to emphasize that metaphors are guidelines to help outsiders quickly understand what members of a culture consider important. This new edition includes a new part structure, three completely new chapters, and major revisions to chapters on American football, Russian ballet, and the Israeli kibbutz. New and Continuing Features: Emphasizes clusters of national cultures and variations within each cluster, as well as both topic-oriented (authority-ranking cultures, market-pricing cultures, etc.) and cluster-focused descriptions. Includes three new parts: India, Shiva, and Diversity; Scandinavian Egalitarian Cultures (Sweden, Denmark, and Finland); and Other Egalitarian Cultures (including Canada and Germany). Provides three completely new chapters: Finnish Sauna, Kaleidoscopic India and Diversity, and a final integrative summary chapter. Integrates chapters through the frameworks of the GLOBE study, the Hofstede study, Hall, and Kluckholn and Strodbeck. Highlights religious and ethnic diversity throughout.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".