Knowledge Sharing in Ontario Colleges: The Way to Sustainable Education
Bibliographic record
Abstract
This paper puts forward several principles that the authors believe are essential for quality education in Canadian colleges. The relationship between establishing communities of practice, creating knowledge repositories, encouraging top management commitment to knowledge sharing and establishing a comprehensive reward system are examined in relation to innovation in education. Sustainable Development Goal (SDG) #4 of the UN postulates quality education among its top initiatives.The question that arises is how do we ensure that SDG #4 is implemented in higher education institutions? Accordingly, data was collected through observation of faculty and staff from the 2017 Ontario Colleges strike. Although a strong corporate culture exists in Ontario colleges, the system continues to struggle with explicit top management principles that support knowledge sharing across different disciplines. Inter and intra departmental forums including students are non-existent. Knowledge repositories, that staff, faculty and students can tap into are lacking. A greater conversation with stakeholders is imperative to weave all the threads of organizational behavior practices together to nurture future global citizens. Only then can we achieve sustainable quality education.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".