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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".