Bibliographic record
Abstract
Abstract This chapter considers some of the challenges of the development of strategy, both for the conventional and ODDE sectors of higher education, with a brief look at the literature since strategic planning was first in vogue in the private sector in the early 1960s. Although the most common approach in higher education, so much so-called strategic planning does little to advance long-term visions and strategies or to differentiate one institution from another. The sudden pivot to online learning and other distance education that the COVID-19 pandemic has forced on conventional (contact) institutions has blurred distinctions between traditional and ODDE universities, thus rendering effective strategy development and implementation more important than ever. This chapter conducts the literature review considering both institutional and system-wide strategy development, underlining their common elements. Then, from the unique vantage point of the South African Institute of Distance Education (Saide), a nongovernmental organization based in Johannesburg but conducting projects throughout South Africa and sub-Saharan Africa, it discusses the challenges for ODDE strategy development in the particular context of COVID-19. The chapter concludes with implications from the analysis for both the conventional and ODDE sectors in higher education in South Africa and elsewhere based, in part, on the lessons learned during the pandemic.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".