Institutional Partnerships and Collaborations in Online Learning
Bibliographic record
Abstract
Abstract Globally, partnerships and collaborations are increasingly common in postsecondary education. The advent of networked technologies has intensified bilateral and multilateral engagements, and in the context of learning, it reveals there are a variety of partnership and collaboration “types” that can form. This chapter presents three examples of partnership and collaboration types drawn from the academic and business literature. Four case studies of partnerships and collaborations are then presented, and the aforementioned types are applied as a best fit to a given case study. The exercise illustrates how partnerships and collaborations in postsecondary education may develop and evolve, and how they can be sustained. The partnership and collaboration types offer some structure to better understand how institutions may approach and derive benefit from engagement with other institutions centered on achieving the objectives of access, quality, and innovation, espoused by proponents of online learning.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".