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Record W3188096994 · doi:10.56059/jl4d.v8i2.507

Developing Partnerships to Acquire Impact: The Role of Three Regional Centres’ Capacity Building Efforts for ODL Adoption in the Emerging World

2021· article· en· W3188096994 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Learning for Development · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education Systems and Policies
Canadian institutionsAthabasca University
Fundersnot available
KeywordsCommonwealthMandateWork (physics)Distance educationCapacity buildingElitePublic relationsPolitical scienceBusinessEconomic growthEngineeringEconomicsPolitics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.123
GPT teacher head0.380
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it