An Extension Early-warning Model of Cross-cultural Conflicts and Its Application
Bibliographic record
Abstract
Aiming at the prevention of cross-cultural conflict crisis,this paper builds an early-warning model of cross-cultural conflicts.Firstly,it employs the extension theory to illustrate warning level evaluation method,as well as the way and mechanism of building an extension early-warning model of cross-cultural conflicts.Then an early-warning evaluation index system of cross-cultural conflict is built,in which the level of each index is classified,and the weight coefficients of the early-warning indexes are determined through questionnaire survey and AHP method.Accordingly,a matter element model of warning level of cross-cultural conflicts is built.Finally,this model is applied to the cross-cultural conflict management practice of a Sino-Canadian joint venture in China.Results of Data Analysis through Matlab show that the proposed model has strong practicality,which provides a strategic management idea and a reliable decision support on how to scientifically prevent cross-cultural conflict crisis for multinational corporations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".