Developing education exchanges between China and the West: The case of Bricknowledge and Mericia
Bibliographic record
Abstract
Institutions are now, more than ever, seeking opportunities to provide global exposure and learning for their students through international exchange. In 2009, three business students from Grant MacEwan University in Edmonton, Alberta, Canada travelled to Beijing to fulfil the practicum requirement of their Asia-Pacific Management program and attain this learning. Their placement was with a non-governmental organisation (NGO) called Bricknowledge Education Group (hereafter Bricknowledge) in China. Bricknowledge was formed in 2003 with the goal of establishing an international platform to address issues facing the development of education in emerging markets and to promote innovation, policy-making and cooperation (and therefore cross-cultural understanding) in the education sector among the BRIC (Brazil, Russia, India and China) nations and developed nations, and in particular between China and the West. The director and staff of Bricknowledge welcomed the MacEwan students and helped them to settle in and adjust to life in Beijing. The students were given the opportunity to learn about the structure of education in China. Importantly, the Chinese government is focused on ensuring quality education, and has sought to raise education levels. As part of their learning, the Grant MacEwan University students analysed the significant similarities and differences, and sought to identify possibilities for collaboration, between North American and Chinese educators, in order to propose ways in which Bricknowledge might develop and capitalise on education exchange opportunities.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".