Developing Partnerships to Acquire Impact: The Role of Three Regional Centres’ Capacity Building Efforts for ODL Adoption in the Emerging World
Bibliographic record
Abstract
Partnerships are central to the awareness, implementation and development of open and distance learning (ODL). It is an attribute that is distinct in the higher education sector, where ODL has made a large footprint by dispelling the notion that university enrolment is reserved for a narrow and elite demographic. The Commonwealth of Learning (COL) operates to advance the uptake of ODL amongst the 54 member states of the Commonwealth. COL leverages its work through various channels, and the COL Regional Centres play a pivotal role as partners to COL and, in turn, to acquire new partners that may benefit from COL’s technical expertise. The Regional Centres, strategically located across the Commonwealth, engage primarily in capacity building for ODL. Their constituents include governments, institutions, and individual learners. This paper explores the role of COL Regional Centres to grow existing partnerships and to form new ones in the pursuit of ODL expansion. The formation of partnerships is understudied in the ODL space, yet it has been pivotal in augmenting the visibility and importance of ODL around the world. Drawing on data from an evaluation of three COL Regional Centres conducted at the end of 2019, and reporting on follow-up activities to the mid-point of 2021, this paper highlights how the RCs are achieving their mandate to engage partners and, in the process, have achieved short- and long-term outcomes since 2018. Findings provide insight into the effectiveness of RC activities, relative to the number of institutions and individuals reached, complemented with inputs from RC stakeholders, mostly comprised of RC staff. Recommendations are offered, with the paper positing that the role of the Regional Centres should continue and expand to other areas of the Commonwealth premised on their ability to build and sustain partnerships through capacity building efforts.
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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 | 0.029 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".