Is Positive Information Less Well Received in Korea versus Canada? Cross-Cultural Comparisons
Bibliographic record
Abstract
긍정적 정보는 항상 호의적 반응을 유발시켜왔다. 하지만, 긍정적 정보일지라도 긍정적 반응이 아닌 다른 반응이 나타날 수 있다. 특히 문화 차이(cultural differences)는 다양한 반응을 유발 시킬 것이다. 본 연구 결과에 따르면, 긍정적 정보에 대해 한국인은 캐다나인보다 높은 수준의 심리적 불편함을 경험하였다. 그 이유는 한국인들이 정보처리과정에서 상황중심의 추론 (situation-based inference)과 모순(laws of contradiction) 을 바탕으로 한 양면추론(dual inference)통해 긍정적 정보의 가치를 낮게 평가하여 수용하기 때문이다. 결과적으로 한국인은 긍정적 정보에 노출된 후, 제품에 대한 낮은 긍정적 태도를 보였으나, 캐나다인은 긍정적 태도가 강화되거나 변화하지(낮아지지) 않았다. 따라서 본 연구는 광고주에 대한 긍정적 정보를 강조하여 제공하는 비교광고가 동양에서 효과가 없었는지를 설명할 수 있다. 또한 긍정적 정보가 반드시 긍정적 결과를 유발시키지도 않을 수 있음을 제시한다.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".