Creating an intelligent evaluation system for cultural intelligence
Bibliographic record
Abstract
In today's global intercultural activities, individuals and organizations must be culturally intelligent. Research on Cultural Intelligence provides a new perspective and a promising means of reducing intercultural conflicts and obstacles. Up until now, research in this domain has used traditional methods in aiding cultural experts, and has relied mainly on questionnaires to test manually the Cultural Intelligence of individuals. This paper provides an opportunity which attempted to improve one's Cultural Intelligence without cultural experts. To reach this goal, a Cultural Intelligence computational model has been created and implemented in an intelligent system, based on an innovative breed of Artificial Intelligence technologies. The purpose of this intelligent system is to support individuals and organizations in solving the intercultural adaptation problems that they face in various authentic situations. The system is considered as highly intelligent due to its wealth of knowledge, openness, scalability, flexibility, adaptability, and capability to self-learn. As a result of these qualities, the system allows better interaction and more effective aid in the evaluation process so as to improve users' cultural skills in different cultural settings in a shorter time.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".