Being a descriptive self and curious self: Comparisons among Chinese immigrants, Chinese Canadians and Euro Canadians
Bibliographic record
Abstract
In the book Clear Leadership, (Bushe, 2001) a set of skills that leads us to the processes of real organizational learning is introduced and described in detail. The Canadian Chinese are an increasing population in Canadian organizations, and research interest has been raised to determine whether Chinese employees have difficulties acquiring the behaviours and skills discussed in the book. In the following study, a comparative method is adopted and behaviours that reflect the Descriptive Self and Curious Self are compared among 55 Chinese immigrants, 42 Canadian born Chinese and 49 European descendents through their responses to four scenarios which demonstrate the standard Descriptive Self and the Curious Self. The results indicate that different cultural heritages create statistically significant differences regarding the extent to which people use clear leadership behaviours. The findings show that almost half of respondents, regardless of ethnic background, say they would use these behaviours. A larger proportion of Chinese, however, are less likely to use Descriptive Self and a Curious Self behaviours than the Canadian Europeans in public settings. In private settings, Canadian born Chinese are significantly more likely to use the behaviours than immigrants, but both groups lag behind Euro- Canadians. While the study supports the view that the Chinese culture creates barriers for Canadian Chinese clear leadership behaviour it also demonstrates that such barriers are not monolithic, and that the effects of culture on behaviour are more complex than accounted for in this study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".