Strategies for sustainable business models for open educational resources
Bibliographic record
Abstract
For several years, the importance of continuous education has been stressed by several governmental and non-governmental institutions (Janssen & Schuwer, 2012; Marshall & Casserly, 2006). Education is seen as important both for personal growth and empowerment for one’s personal wellbeing as well as for developing the required professional capabilities needed in today’s society. In his 2011 State of the Union address President Obama put emphasis on the government’s ambitions to “out-innovate and out-educate” the rest of the world. Almost at the same time, at the Davos World Economic Forum (2011), the urgency of appropriate education was stressed, observing that the current lack of adequately educated people hinders prosperity and economic growth in the near future. The OECD is preparing a proposal to translate these intentions into a concrete policy.
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 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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".