Can Online Short Courses Foster Business Education for Sustainable Development?
Bibliographic record
Abstract
The COVID-19 pandemic challenged the practice of traditional higher education providers (HEPs) and highlighted the need for innovative approaches to education for sustainable development. This research note focuses on online short courses (OSCs)—micro-credentials geared at upskilling or reskilling learners with a competitive application process and cost. It conducts (a) a rapid bibliometric analysis of literature on the nexus between OSCs and sustainable development and (b) an environmental scan of OSCs offered in Australia with a lens of sustainable development. An exploratory approach was adopted to analyze publicly available secondary data on scholarly literature and the courses offered. Findings reveal two key trends: (i) the nascent nature of literature on OSCs and sustainable development globally and (ii) the limited availability of sustainable development related OSCs in Australia. This research note makes broad analytical contributions to posit OSCs as an e-learning innovation to advance business education for sustainable development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".