Values and Attitudes towards Innovation among Canadian, Chinese and Russian Students
Bibliographic record
Abstract
This study investigated relations of basic personal values to attitudes towards innovation among students in Russia, Canada, and Ñhina. Participants completed a questionnaire that included the SVS measure of values (Schwartz, 1992) and a new measure of attitudes towards innovation (Lebedeva, Tatarko, 2009). There are significant cultural and gender-related differences in value priorities and innovative attitudes among the Canadian, Russian, and Chinese college students. As hypothesized, across the full set of participants, higher priority given to Opennes to change values (self-direction, stimulation) related to positive attitudes toward innovation whereas higher priority given to Conservation values (conformity, security) related negatively. This is compatible with the results reported by other researchers (Shane, 1992, 1995; Dollinger, Burke & Gump, 2007). There were, however, culture-specific variations in some of these associations, which may be explained by cultural differences in value priorities or meanings and in implicit theories of creativity and innovation. Applying the Multiple-Group Multiple Indicators Multiple Causes Model (MGMIMIC) (Muthen 1989) has shown that the type of Values-Innovation mediation is different in the three countries. Whereas in Russia and Canada the effects of gender and age are fully mediated by the values, this is not true for China, where a direct effect of gender on innovation was found. The cultural differences in values, implicit theories of innovation, and their consequences for attitudes to innovation and personal well-being is finally 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".