Building bridges: a case study of the development and sustainability of an international partnership in post-secondary engineering education
Bibliographic record
Abstract
In a world that is becoming increasingly globalized, a change in the nature of higher education is leading to increased collaboration across borders. In a sector once dominated by developed countries, developing countries are becoming larger players in providing education to their countries and expanding into the field of providing education for others. This partnership began in 1992 when Manitoba was experiencing reduced government funding, frozen tuition rates and declining student numbers while Malaysia was building the capacity of their higher education system. An inter-institutional partnership was established to create a mutually beneficial relationship between the Faculty of Engineering, University of Manitoba, and University College Sedaya International (UCSI) in Malaysia. This partnership was established with clear cut benefits to both parties and developed over time with close personal ties between the institutions. However, the partnership has been declining since 2003 when UCSI was permitted to grant degrees. The lifecycle of the partnership is examined in light of this structural change. Organization models of episodic change and punctuated equilibrium, and transformative learning theory are used to explain the status of the partnership and the options for its sustainability.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".