Chinese and Canadian Teachers’ Perspectives on Educational Policy Changes Technical Report of Results from the Survey of Canadian and Chinese Teachers’ Attitudes towards Policy Changes in their School Education Systems
Bibliographic record
Abstract
This survey of Canadian and Chinese teachers perceptions of educational change is a component of the Canadian Social Science and Humanties Research Council-supported Reciprocal Learning Partnership Project (RLPP). The RLPP was built around a set of sister-schools in several Chinese cities and in Toronto and Windsor, Ontario as well a teacher-education project involving schools in Windsor and Chongqing. The goal was to secure an understanding of the differences and similarities in the attitudes of the sister-schools' respondents towards recent policy changes, and in particular internationalization, in their jurisdictions. \n\tA convenience sample of 407 teachers (Canada, N=149; China, N=258) from schools in Beijing (2 schools), Shanghai (2), Chongqing (5), Changchun (1), Anshan (1), and Windsor, Ontario (7) responded to the survey. The survey itself was built around a set of Likert scales probing the buzz-words associated with contemporary education policy-making. \nWe found that, overall, there was considerable similarity of the responses of the two populations—with the modal response being between “Neutral” and “Agree.” However, within the limitations of this narrow band of responses we see two kinds of pattern in the findings. On the one hand, the Chinese teachers were, overall, more positive and less discriminating in their support for what their systems were doing than were their Canadian counterparts. On the other hand, when we compared each set of respondents’ priorities with their perceptions of the impact of their systems’ policy-making in the areas reflected in the questionnaire’s sets of buzzwords, the differences were striking. The Canadian respondents reported greater congruity between their priorities for the school and classroom and for sectorial and system policy-making and their perception of the impact of their system’s policy-making on these areas than did the Chinese respondents. \nWe also asked the respondents to indicate their priorities for new policies and the areas of impact of their systems’ policy-making. Predictably both sets of respondents prioritized the buzzwords or slogans in the questions that referred to the transactional aspects of teachers’ work; Student engagement and Curriculum content and objectives along with Educational equity were the highest ranked buzzword.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".