Bibliographic record
Abstract
The definition of openness influenced the sustainability of business models of Open Education (OE). Yet, whether openness is defined as the free (re)usage of resources, or the free entry in courses, there always is a discussion on who pays for the resources used in these offerings. The free offering of courses or materials raises the question if OE can be maintained independent of large government subsidies. This article analyzes four cases that each have a different approach to OE and (financial) survival. The aim of this study is to determine the most efficient conditions for a sustainable OE business model.Instead of using different earning models, this research concentrates on the different aspects of unbundling (costs, income, and financiers), arguing that an adjusted Business Model Canvas can be used to analyze the not-for-profit organizations in higher education institutions (HEIs). The cases are OpenupEd, FemTechNet, MERLOT, and Lumen Learning. Openness plays different roles in the business models of the different organizations. For OpenupEd and MERLOT, openness of the materials offered to students and teachers (MOOCs, OER) is essential. For FemTechNet, openness is part of the need to collaborate and share within their community. Commercial organizations, such as Lumen Learning, use free materials to teach educational organizations to use these materials for their own courses. All four organizations use different key activities and key resources (for example, management competencies, social skills, or design and teaching skills) for their continuity. Yet, despite the differences between the case-organizations, community building is important in all cases. Either because producers and users of Open Education become identical, because standardization does decrease costs and increases findability and quality, or because they can bridge the difference between supply and competences necessary for usage of Open Education.
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.
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 | 0.007 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".