Supporting the Contribution of Higher Education Institutions \nto Regional Development
Bibliographic record
Abstract
From 2005 to 2007, Memorial University of Newfoundland (MUN) and the College of the North Atlantic (CNA) participated, with the other Atlantic Provinces, in the Organization for Economic Cooperation and Development’s (OECD’s) 12-country, 14-region study on the contribution of higher education institutions (HEIs) to regional development. That study attempted to strengthen the contributions of HEIs to regional/local development by improving interplay and mutual capacity building between HEIs and regional/provincial/local stakeholders and to raise awareness that the role of HEIs extends beyond the core competencies of knowledge generation (research) and knowledge transfer (teaching) to a third function, knowledge mobilization (regional/local engagement). \n \nThis OECD exercise yielded vast amounts of useful information, which may be intimidating and may not be as accessible to those who can make good use of this research. To facilitate its use in the Newfoundland and Labrador (NL) context, this report has reviewed, evaluated and synthesized the relevant information to determine what lessons NL can take from the OECD exercise. \n \nBy applying these lessons locally, both the Atlantic Region and the province can enhance the role of our universities and community colleges as agents of economic and social growth. While NL’s HEIs have performed well both in absolute terms and relative to HEIs in the other jurisdictions studied, it is important to recognize that the effectiveness of our universities and community colleges in facilitating regional/local engagement can be enhanced by implementing the positive initiatives and avoiding the negative lessons that fall out of the OECD study. \n \nThis report evaluates how NL, MUN and the CNA are doing in terms of regional engagement and it offers illustrations of successful regional engagement initiatives found in NL. In addition, this report profiles regional engagement practices found in the OECD countries studied. \n \nThere is a growing awareness around the world of the importance of local HEIs engaging local stakeholders and applying some of the institutions’ intellectual capital to issues and problems that are important locally. Sometimes this awareness simply translates into lip service and does not become a tangible approach to promote institutional engagement. Consequently, rather than expressing the right sentiments, bolstered by the latest buzzwords, it is important to implement meaningful and specific changes within the HEIs. \n \n
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".