What Do National Flags Stand for? An Exploration of Associations Across 11 Countries
Bibliographic record
Abstract
We examined the concepts and emotions people associate with their national flag, and how these associations are related to nationalism and patriotism across 11 countries. Factor analyses indicated that the structures of associations differed across countries in ways that reflect their idiosyncratic historical developments. Positive emotions and egalitarian concepts were associated with national flags across countries. However, notable differences between countries were found due to historical politics. In societies known for being peaceful and open-minded (e.g., Canada, Scotland), egalitarianism was separable from honor-related concepts and associated with the flag; in countries that were currently involved in struggles for independence (e.g., Scotland) and countries with an imperialist past (the United Kingdom), the flag was strongly associated with power-related concepts; in countries with a negative past (e.g., Germany), the primary association was sports; in countries with disruption due to separatist or extremist movements (e.g., Northern Ireland, Turkey), associations referring to aggression were not fully rejected; in collectivist societies (India, Singapore), obedience was linked to positive associations and strongly associated with the flag. In addition, the more strongly individuals endorsed nationalism and patriotism, the more they associated positive emotions and egalitarian concepts with their flag. Implications of these findings are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".